To Appear in Publications of the Astronomical Society of the Pacific
Preprint typeset using LATEX style emulateapj v. 08/22/09
CALFUSE v3: A DATA-REDUCTION PIPELINE FOR THE FAR ULTRAVIOLET SPECTROSCOPIC
EXPLORER1
W. V. Dixon
, D. J. Sahnow
, P. E. Barrett
, T. Civeit
, J. Dupuis
, A. W. Fullerton
, B. Godard
J.-C. Hsu
, M. E. Kaiser
, J. W. Kruk
, S. Lacour
, D. J. Lindler
, D. Massa
, R. D. Robinson
M. L. Romelfanger
, and P. Sonnentrucker
To Appear in Publications of the Astronomical Society of the Pacific
ABSTRACT
Since its launch in 1999, the Far Ultraviolet Spectroscopic Explorer (FUSE) has made over 4600
observations of some 2500 individual targets. The data are reduced by the Principal Investigator
team at the Johns Hopkins University and archived at the Multimission Archive at Space Telescope
(MAST). The data-reduction software package, called CalFUSE, has evolved considerably over the
lifetime of the mission. The entire FUSE data set has recently been reprocessed with CalFUSE v3.2,
the latest version of this software. This paper describes CalFUSE v3.2, the instrument calibrations
upon which it is based, and the format of the resulting calibrated data files.
Subject headings: instrumentation: spectrographs — methods: data analysis — space vehicles: in-
struments — ultraviolet: general — white dwarfs
1. INTRODUCTION
The Far Ultraviolet Spectroscopic Explorer (FUSE) is a
high-resolution, far-ultraviolet spectrometer operating in
the 905–1187 Å wavelength range. FUSE was launched
in 1999 on a Delta II rocket into a nearly circular, low-
earth orbit with an inclination of 25◦ to the equator and
an approximately 100-minute orbital period. Data ob-
tained with the instrument are reduced by the principal
investigator team at the Johns Hopkins University using
a suite of computer programs called CalFUSE. Both raw
and processed data files are deposited in the Multimis-
sion Archive at Space Telescope (MAST).
CalFUSE evolved considerably in the years following
launch as our increasing knowledge of the spectrograph’s
performance allowed us to correct the data for more and
more instrumental effects. The program eventually be-
came unwieldy, and in 2002 we began a project to re-
write the code, incorporating our new understanding of
the instrument and best practices for data reduction.
1 Based on observations made with the NASA-CNES-CSA Far
Ultraviolet Spectroscopic Explorer. FUSE is operated for NASA by
the Johns Hopkins University under NASA contract NAS5-32985.
2 Department of Physics and Astronomy, Johns Hopkins Univer-
sity, 3400 N. Charles Street, Baltimore, MD 21218
3 Space Telescope Science Institute, ESS/SSG, 3700 San Martin
Drive, Baltimore, MD 21218
4 Current address: Earth Orientation Department, U.S. Naval
Observatory, 3450 Massachusetts Avenue NW, Washington, DC
20392
5 Primary affiliation: Centre National d’Études Spatiales, 2 place
Maurice Quentin, 75039 Paris Cedex 1, France
6 Current address: Canadian Space Agency, 6767 route de
l’Aéroport, Longueuil, QC, Canada, J3Y 8Y9
7 Primary affiliation: Department of Physics and Astronomy,
University of Victoria, P. O. Box 3055, Victoria, BC V8W 3P6,
Canada
8 Current address: Institut d’Astrophysique de Paris, 98 bis,
boulevard Arago, 75014 Paris, France
9 Retired
10 Current address: Sydney University, NSW 2006, Australia
11 Sigma Space Corporation, 4801 Forbes Boulevard, Lanham,
MD 20706
12 SGT, Inc., NASA Goddard Space Flight Center, Code 665.0,
Greenbelt, MD 20771
The result is CalFUSE v3, which produces a higher qual-
ity of calibrated data while running ten times faster than
previous versions. The entire FUSE archive has recently
been reprocessed with CalFUSE v3.2; we expect this to
be the final calibration of these data.
In this paper, we describe CalFUSE v3.2.0 and its cal-
ibrated data products. Because this document is meant
to serve as a resource for researchers analyzing archival
FUSE spectra, we emphasize the interpretation of pro-
cessed data files obtained fromMAST rather than the de-
tails of designing or running the pipeline. An overview of
the FUSE instrument is provided in § 2, and an overview
of the pipeline in § 3. Section 4 presents a detailed de-
scription of the pipeline modules and their subroutines.
The FUSE wavelength and flux calibration are discussed
in § 5, and a few additional topics are considered in § 6. A
detailed description of the various file formats employed
by CalFUSE is presented in the Appendix.
Additional documentation available from MAST in-
cludes the CalFUSE Homepage,13 The CalFUSE
Pipeline Reference Guide,14 The FUSE Instrument and
Data Handbook,15 and The FUSE Data Analysis Cook-
book.16
2. THE FUSE INSTRUMENT
FUSE consists of four co-aligned prime-focus tele-
scopes, each with its own Rowland spectrograph (Fig. 1).
Two of the four channels employ Al+LiF optical coatings
and record spectra over the wavelength range∼ 990–1187
Å, while the other two use SiC coatings, which provide
reflectivity to wavelengths below the Lyman limit. The
four channels overlap between 990 and 1070 Å. Spectral
resolution is roughly 20,000 (λ/∆λ) for point sources.
For a complete description of FUSE, see Moos et al.
(2000) and Sahnow et al. (2000a).
At the prime focus of each mirror lies a focal-plane as-
13 http://archive.stsci.edu/fuse/calfuse.html
14 http://archive.stsci.edu/fuse/pipeline.html
15 http://archive.stsci.edu/fuse/dhbook.html
16 http://archive.stsci.edu/fuse/cookbook.html
http://arxiv.org/abs/0704.0899v1
http://archive.stsci.edu/fuse/calfuse.html
http://archive.stsci.edu/fuse/pipeline.html
http://archive.stsci.edu/fuse/dhbook.html
http://archive.stsci.edu/fuse/cookbook.html
2 Dixon et al.
sembly (or FPA, shown in Fig. 2) containing three spec-
trograph entrance apertures: the low-resolution aperture
(LWRS; 30′′ × 30′′), used for most observations, the
medium-resolution aperture (MDRS; 4′′ × 20′′), and the
high-resolution aperture (HIRS; 1.25′′ × 20′′). The ref-
erence point (RFPT) is not an aperture; when a target
is placed at this location, the three apertures sample the
background sky. For a particular exposure, the FITS
file header keywords RA TARG and DEC TARG con-
tain the J2000 coordinates of the aperture (or RFPT)
listed in the APERTURE keyword, while the keyword
APER PA contains the position angle of the −Y axis (in
the FPA coordinate system; see Fig. 2), corresponding to
a counter-clockwise rotation of the spacecraft about the
target (and thus about the center of the target aperture).
The spectra from the four instrument channels are
imaged onto two photon-counting microchannel-plate
(MCP) detectors, labeled 1 and 2, with a LiF spectrum
and a SiC spectrum on each (Fig. 1). Each detector is
comprised of two MCP segments, labeled A and B. Raw
science data from each detector segment are stored in
a separate data file; an exposure thus yields four raw
data files, labeled 1A, 1B, 2A, and 2B. Because the three
apertures are open to the sky at all times, the LiF and
SiC channels each generate three spectra, one from each
aperture. In most cases, the non-target apertures are
empty and sample the background sky. Figure 3 presents
a fully-corrected image of detector 1A obtained during
a bright-earth observation. The emission features in all
three apertures are geocoronal. Note that the LiF1 wave-
length scale increases to the right, while the SiC1 scale
increases to the left. The Lyman β λ1026 airglow feature
is prominent in each aperture.
Two observing modes are available: In photon-address
mode, also known as time-tag or TTAG mode, the X and
Y coordinates and pulse height (§ 4.3.7) of each detected
photon are stored in a photon-event list. A time stamp
is inserted into the data stream, typically once per sec-
ond. Data from the entire active area of the detector are
recorded. Observing bright targets in time-tag mode can
rapidly fill the spacecraft recorder. Consequently, when
a target is expected to generate more than ∼ 2500 counts
s−1 across all four detector segments, the data are stored
in spectral-image mode, also called histogram or HIST
mode. To conserve memory, histogram data are (usu-
ally) binned by eight pixels in Y (the spatial dimension),
but unbinned in X (the dispersion dimension). Only data
obtained through the target aperture are recorded. Indi-
vidual photon arrival time and pulse height information
is lost. The orbital velocity of the FUSE spacecraft is 7.5
km s−1. Since Doppler compensation is not performed
by the detector electronics, histogram exposures must be
kept short to preserve spectral resolution; a typical his-
togram exposure is about 500 s in length.
The front surfaces of the FPAs are reflective in visible
light. On the two LiF channels, light not passing through
the apertures is reflected into a visible-light CCD camera.
Images of stars in the field of view around the apertures
are used for acquisition and guiding by this camera sys-
tem, called the Fine Error Sensor (FES). FUSE carries
two redundant FES cameras, which were provided by
the Canadian Space Agency. FES A views the FPA on
the LiF1 channel, and FES B views the LiF2 FPA. Dur-
ing initial checkout, FES A was designated the default
camera and was used for all science observations until it
began to malfunction in 2005. In July of that year, FES
B was made the default guide camera. Implications of
the switch from FES A to FES B are discussed in § 6.1.
3. OVERVIEW OF CALFUSE
The new CalFUSE pipeline was designed with three
principles mind: the first was that, to the extent possi-
ble, we follow the path of a photon backwards through
the instrument, correcting for the instrumental effects in-
troduced in each step. The principal steps in this path,
together with the effects imparted by each, are listed be-
low. Most of the optical and electronic components in
this list are labeled in Fig. 1.
1. Satellite motion imparts a Doppler shift.
2. Satellite pointing instabilities shift the target image
within (or out of) the aperture.
3. Thermally-induced mirror motions shift the target im-
age within (or out of) the aperture.
4. FPA offsets shift the spectrum on the detector.
5. Thermally-induced motions of the spectrograph grat-
ings shift the target image within (or out of) the aper-
ture.
6. Ion-repelling wire grids can cast shadows called
“worms.”
7. Detector effects include quantum efficiency, flat field,
dead spots, and background.
8. The spectra are distorted by temperature-, count-rate,
time-, and pulse-height-dependent errors in the photons’
measured X and Y coordinates, as well as smaller-scale
geometric distortions in the detector image.
9. Count-rate limitations in the detector electronics and
the IDS data bus are sources of dead time.
To correct for these effects, we begin at the bottom
of the list and (to the extent possible) work backwards.
First, we adjust the photon weights to account for data
lost to dead time (9) and correct the photons’ X and
Y coordinates for a variety of detector distortions (8).
Second, we identify periods of unreliable, contaminated,
or missing data. Third, we correct the photons’ X and
Y coordinates for grating (5), FPA (4), mirror (3), and
spacecraft (2) motions. Fourth, we assign a wavelength
to each photon based on its corrected X and Y coor-
dinates (5), then convert to a heliocentric wavelength
scale (1). Finally, we correct for detector dead spots
(7); model and subtract the detector and scattered-light
backgrounds (7); and extract (using optimal extraction,
if possible), flux calibrate (7) and write to separate FITS
files the target’s LiF and SiC spectra. Note that we can-
not correct for the effects of worms (6) or the detector
flat field (7).
Our second principal was to make the pipeline as mod-
ular as possible. CalFUSE is written in the C program-
ming language and runs on the Solaris, Linux, and Mac
OS X (versions 10.2 and higher) operating systems. The
pipeline consists of a series of modules called by a shell
script. Individual modules may be executed from the
command line. Each performs a set of related correc-
tions (screen data, remove motions, etc.) by calling a
series of subroutines.
Our third principal was to maintain the data as a pho-
ton list (called an intermediate data file, or IDF) un-
til the final module of the pipeline. Input arrays are
read from the IDF at the beginning of each module, and
CalFUSE: The FUSE Calibration Pipeline 3
output arrays are written at the end. Bad photons are
flagged but not discarded, so the user can examine, fil-
ter, and combine processed data files without re-running
the pipeline. Like all FUSE data, IDFs are stored as
FITS files (Hanisch et al. 2001); the various file formats
employed by CalFUSE are described in the Appendix.
A FUSE observation consists of a set of exposures ob-
tained with a particular target in a particular aperture
on a particular date. Each exposure generates four raw
data files, one per detector segment, and each raw data
file yields a pair of calibrated spectra (LiF and SiC), for
a total of 8 calibrated spectral files per exposure. Each
raw data file is processed individually by the pipeline.
Error and status messages are written to a trailer file
(described in § 4.10). Spectra are extracted only for
the target aperture and are binned in wavelength. Bin-
ning can be set by the user, but the default is 0.013 Å,
which corresponds to about two detector pixels or one
fourth of a point-source resolution element. After pro-
cessing, additional software is used to generate a set of
observation-level spectral files, the ALL, ANO, and NVO
files described in § 4.11. A complete list of FUSE data
files and file-naming conventions may be found in The
FUSE Instrument and Data Handbook. All of the expo-
sures that constitute an observation are processed and
archived together.
Investigators who wish to re-process their data may
retrieve the CalFUSE source code and all associated cal-
ibration files from the CalFUSE Homepage. Instructions
for running the pipeline and detailed descriptions of the
calibration files are provided in The CalFUSE Pipeline
Reference Guide. Note that, within the CalFUSE soft-
ware distribution, all of the calibration files, including
the FUSE.TLE file (§ 4.2), are stored in the directory
v3.2/calfiles, while all of the parameter files, including
master calib file.dat and the screening and parameter
files (SCRN CAL and PARM CAL; § 4.2), are stored
in the directory v3.2/parmfiles.
4. STEP BY STEP
In this section, we discuss the pipeline subroutine
by subroutine. Our goal is to describe the algorithms
employed by each subroutine and any shortcomings or
caveats of which the user should be aware.
4.1. OPUS
The Operations Pipeline Unified System (OPUS) is
the data-processing system used by the Space Telescope
Science Institute to reduce science data from the Hub-
ble Space Telescope (HST). We use a FUSE-specific ver-
sion of OPUS to manage our data processing (Rose et al.
1998). OPUS ingests the data downlinked by the space-
craft and produces the data files that serve as input to
the CalFUSE pipeline. OPUS then manages the execu-
tion of the pipeline and the files produced by CalFUSE
and calls the additional routines that combine spectra
from each channel and exposure into a set of observation-
level spectral files. OPUS reads the FUSE Mission Plan-
ning Database (which contains target information from
the individual observing proposals and instrument con-
figuration and scheduling information from the mission
timeline) to populate raw file header keywords and to
verify that all of the data expected from an observation
were obtained.
OPUS generates six data files for each exposure. Four
are raw data files (identified by the suffix “fraw.fit”), one
for each detector segment. One is a housekeeping file
(“hskpf.fit”) containing time-dependent spacecraft engi-
neering data. Included in this file are detector volt-
ages, count rates, and spacecraft-pointing information.
The housekeeping file is used to generate a jitter file
(“jitrf.fit”), which contains information needed to cor-
rect the data for spacecraft motion during an exposure.
Detailed information on the format and contents of each
file is provided in the Appendix.
4.2. Generate the Intermediate Data File
The first task of the pipeline is to convert the raw data
file into an intermediate data file (IDF), which maintains
the data in the form of a photon-event list. (The format
and contents of the IDF are described in § A-3.) For data
obtained in time-tag mode, the module cf ttag init
merely copies the arrival time, X and Y detector coor-
dinates, and pulse-height of each photon event from the
raw file to the TIME, XRAW, YRAW, and PHA arrays of
the IDF. A fifth array, the photon weight, is initially set
to unity. Photons whose X and Y coordinates place them
outside of the active region of the detector are flagged as
described in § 4.3.8. Raw histogram data are stored by
OPUS as an image; the module cf hist init converts
each non-zero pixel of that image into a single entry in
the IDF, with X and Y equal to the pixel coordinates
(mapped to their location on the unbinned detector),
arrival time set to the mid-point of the exposure, and
pulse height set to 20 (possible values range from 0 to
31). The arrival time and pulse height are modified later
in the pipeline. The photon weight is set to the number
of accumulated counts on the pixel, i.e., the number of
photons detected on that region of the detector.
The IDF has two additional extensions. The first con-
tains the good-time intervals (GTIs), a series of start
and stop times (in seconds from the exposure start time
recorded in the file header) computed by OPUS, when
the data are thought to be valid. For time-tag data, this
extension is copied directly from the raw data file. For
histogram data, a single GTI is generated with START =
0 and STOP = EXPTIME (the exposure time computed
by OPUS). The final extension is called the timeline ta-
ble and consists of 16 arrays containing status flags and
spacecraft-position, detector high-voltage, and count-
rate parameters tabulated once per second throughout
the exposure. Only the day/night and OPUS bits of the
time-dependent status flags are populated (§ A-3); the
others are initialized to zero. The spacecraft-position pa-
rameters are computed as described below. The detector
voltages and the values of various counters are read from
the housekeeping data file.
A critical step in the initialization of the IDF is pop-
ulating the file-header keywords that describe the space-
craft’s orbit and control the subsequent actions of the
pipeline. The names of all calibration files to be used by
the pipeline are read from the file master calib file.dat
and written to file-header keywords. (Keywords for each
calibration file are included in the discussion that fol-
lows.) Three sets of calibration files are time-dependent:
the effective area is interpolated from the two files
with effective dates immediately preceding and follow-
ing the exposure start time (these file names are stored
4 Dixon et al.
in the header keywords AEFF1CAL and AEFF2CAL);
the scattered-light model is taken from the file with an
effective date immediately preceding the exposure start
time (keyword BKGD CAL); and the orbital elements
are read from the FUSE.TLE file, an ASCII file contain-
ing NORAD two-line elements for each day of the mis-
sion. These two-line elements are used to populate both
the orbital ephemeris keywords in the IDF file header and
the various spacecraft-position arrays in the timeline ta-
ble. Finally, a series of data-processing keywords is set
to either PERFORM or OMIT the subsequent steps of
the pipeline. Once a step is performed, the correspond-
ing keyword is set to COMPLETE. Some user control of
the pipeline is provided by the screening and parameter
files (SCRN CAL and BKGD CAL), which allow one,
for example, to select only night-time data or to turn
off background subtraction. An annotated list of file-
header keywords, including the calibration files used by
the pipeline, is provided in the FUSE Instrument and
Data Handbook.
Caveats: Occasionally, photon arrival times in raw
time-tag data files are corrupted. When this happens,
some fraction of the photon events have identical, enor-
mous TIME values, and the good-time intervals contain
an entry with START and STOP set to the same large
value. The longest valid exposure spans 55 ks (though
most are ∼ 2 ks long). If an entry in the GTI table ex-
ceeds this value, the corresponding entry in the timeline
table is flagged as bad (using the “photon arrival time
unknown” flag; § A-3). Bad TIME values less than 55 ks
will not be detected by the pipeline.
Raw histogram files may also be corrupted. OPUS fills
missing pixels in a histogram image with the value 21865.
The pipeline sets the WEIGHT of such pixels to zero and
flags them as bad (by setting the photon’s “fill-data bit”;
§ A-3). Occasionally, a single bit in a histogram image
pixel is flipped, producing (for high-order bits) a “hot
pixel” in the image. The pipeline searches for pixels with
values greater than 8 times the average of their neighbors,
identifies the flipped bit, and resets it.
One or more image extensions may be missing from a
raw histogram file (§A-2). If no extensions are present,
the keyword EXP STAT in the IDF header is set to −1.
Exposures with non-zero values of EXP STAT are pro-
cessed normally by the pipeline, but are not included in
the observation-level spectral files ultimately delivered to
MAST (§ 4.11). Though the file contains no data, the
header keyword EXPTIME is not set to zero.
Early versions of the CalFUSE pipeline did not make
use of the housekeeping files, but instead employed engi-
neering information downloaded every five minutes in a
special “engineering snapshot” file. That information is
used by OPUS to populate a variety of header keywords
in the raw data file. If a housekeeping file is not avail-
able, CalFUSE v3 uses these keywords to generate the
detector high-voltage and count-rate arrays in the time-
line table. Should these header keywords be corrupted,
the pipeline issues a warning and attempts to estimate
the corrupted values. In such cases, it is wise to compare
the resulting dead-time corrections (§ 4.3.2) with those
of other, uncorrupted exposures of the same target.
4.3. Convert to FARF
The pipeline module cf convert to farf is designed
to remove detector artifacts. Our goal is to construct
the data set that would be obtained with an ideal detec-
tor. The corrections can be grouped into two categories:
dead-time effects, which are system limitations that re-
sult in the loss of photon events recorded by the detec-
tor, and positional inaccuracies, i.e., errors in the raw
X and Y pixel coordinates of individual photon events.
The coordinate system defined by these corrections is
called the flight alignment reference frame, or FARF.
Corrected coordinates for each photon event are written
to the XFARF and YFARF arrays of the IDF.
4.3.1. Digitizer Keywords
The first subroutine of this module,
cf check digitizer, merely compares a set of 16
IDF file header keywords, which record various detector
settings, with reference values stored in the calibration
file DIGI CAL. Significant differences result in warning
messages being written to both the file header and the
exposure trailer file. Such warning messages should be
taken seriously, as data obtained when the detectors are
not properly configured are likely to be unusable. Be-
sides issuing a warning, the program sets the EXP STAT
keyword in the IDF header to −2.
4.3.2. Detector Dead Time
The term “dead time” refers specifically to the finite
time interval required by the detector electronics to pro-
cess a photon event. During this interval, the detector is
“dead” to incoming photons. The term is more generally
applied to any loss of data that is count-rate dependent.
There are three major contributions to the effective de-
tector dead time on FUSE. The first is due to limitations
in the detector electronics, which at high count rates may
not be able to process photon events as fast as they ar-
rive. The correction for this effect is computed separately
for each segment from the count rate measured at the
detector anode by the Fast Event Counter (FEC) and
recorded to the engineering data stream, typically once
every 16 seconds. The functional form of the correction
was provided by the detector development group at the
University of California, Berkeley, and its numerical con-
stants were determined from in-flight calibration data. It
is applied by the subroutine cf electronics dead time.
A second contribution to the dead time comes from
the way that the Instrument Data System (IDS) pro-
cesses counts coming from the detector. The IDS can
accept at most 8000 counts per second in time-tag mode
and 32000 counts per second in histogram mode from
the four detector segments (combined). At higher count
rates, photon events are lost. To correct for such losses,
the subroutine cf ids dead time compares the Active
Image Counter (AIC) count rate, measured at the back
end of the detector electronics, with the maximum al-
lowed rate. The IDS dead-time correction is the ratio of
these two numbers (or unity, whichever is greater).
A third contribution occurs when time-tag data are
bundled into 64 kB data blocks in the IDS bulk memory.
This memory is organized as a software FIFO (first-in,
first-out) memory buffer, and the maximum data transfer
rate from it to the spacecraft recorder (the FIFO drain
rate) is approximately 3500 events per second. At higher
count rates, the FIFO will eventually fill, resulting in the
CalFUSE: The FUSE Calibration Pipeline 5
loss of one or more data blocks. The effect appears as
a series of data drop-outs, each a few seconds in length,
in the raw data files. The correction, computed by the
subroutine cf fifo dead time, is simply the ratio of the
AIC count rate to the FIFO drain rate. When triggered,
this correction incorporates (and replaces) the IDS cor-
rection discussed above.
The total dead-time correction (always ≥ 1.0) is
simply the product of the detector electronics and
IDS corrections. It is computed (by the subroutine
cf apply dead time) once each second and applied to
the data by scaling the WEIGHT associated with each
photon event. The mean value of the detector electron-
ics, IDS, and total dead-time corrections are stored in
the DET DEAD, IDS DEAD, and TOT DEAD header
keywords, respectively. Other possible sources of dead
time, such as losses due to the finite response time of the
MCPs, have a much smaller effect and are ignored.
Caveats: Our dead-time correction algorithms are in-
appropriate for very bright targets. If the header key-
word TOT DEAD > 1.5, then the exposure should not
be considered photometric. If the housekeeping file for
a particular exposure is missing, the file header key-
words from which the count rates are calculated appear
to be corrupted, and either DET DEAD or IDS DEAD
is > 1.5, then the dead-time correction is assumed to be
meaningless and is set to unity. In both of these cases,
warning messages are written to the file header and the
trailer file.
4.3.3. Temperature-Dependent Changes in Detector
Coordinates
The X and Y coordinates of a photon event do not
correspond to a physical pixel on the detector, but
are calculated from timing and voltage measurements
of the incoming charge cloud (Siegmund et al. 1997;
Sahnow et al. 2000b). As a result, the detector coor-
dinate system is subject to drifts in the detector elec-
tronics caused by temperature changes and other effects.
To track these drifts, two signals are periodically injected
into the detector electronics. These “stim pulses” appear
near the upper left and upper right corners of each de-
tector, outside of the active spectral region. The stim
pulses are well placed for tracking changes in the scale
and offset of the X coordinate, but they are not well
enough separated in Y to track scale changes along that
axis. The subroutine cf thermal distort determines
the X and Y centroids of the stim pulses, computes the
linear transformation necessary to move them to their
reference positions, and applies that transformation to
the X and Y coordinates of each photon event in the re-
gions of the stim pulses and in the active region of the
detector. Events falling within 64 pixels (in X and Y)
of the expected stim-pulse positions are flagged by set-
ting the stim-pulse bit in the LOC FLGS array (§ A-3).
In raw histogram files, the stim pulses are stored in a
pair of image extensions. If either of these extensions is
missing, the pipeline reads the expected positions of the
stim pulses from the calibration file STIM CAL and ap-
plies the corresponding correction. This works (to first
order) because the stim pulses drift slowly with time,
though short-timescale variations cannot be corrected if
the stim pulses are absent.
4.3.4. Count-Rate Dependent Changes in Detector Y Scale
For reasons not yet understood, the detector Y scale
varies with the count rate, in the sense that the detector
image for a high count-rate exposure is expanded in Y. To
measure this effect, we tabulated the positions of individ-
ual detector features (particularly bad-pixel regions) as a
function of the FEC count rate (§ 4.3.2) and determined
the Y corrections necessary to shift each detector feature
to its observed position in a low count-rate exposure.
From this information, we derived the calibration file
RATE CAL for each detector segment. The correction
is stored as a two-dimensional image: the first dimension
represents the count rate and the second is the observed
Y pixel value. The value of each image pixel is the Y shift
(in pixels) necessary to move a photon to its corrected
position. The subroutine cf count rate y distort ap-
plies this correction to each photon event in the active
region of the detector. For time-tag data, the FEC count
rate is used to compute a time- and Y-dependent correc-
tion; for histogram data, the weighted mean of the FEC
count rate is used to derive a set of shifts that depends
only on Y.
4.3.5. Time-Dependent Changes in Detector Coordinates
As the detector and its electronics age, their proper-
ties change, resulting in small drifts in the computed
coordinates of photon events. These changes are most
apparent in the Lyman β airglow features observed in
each of the three apertures of the LiF and SiC channels
(Fig. 3), which drift slowly apart in Y as the mission pro-
gresses, indicating a time-dependent stretch in the detec-
tor Y scale. To correct for this stretch, the subroutine
cf time xy distort applies a time-dependent correction
(stored in the calibration file TMXY CAL) to the Y co-
ordinate of each photon event in the active region of the
detector.
Caveats: Although there is likely to be a similar change
to the X coordinate, no measurement of time-dependent
drifts in that dimension is available, so no correction is
applied.
4.3.6. Geometric Distortion
In an image of the detector generated from raw X and
Y coordinates, the spectrum is not straight, but wig-
gles in the Y dimension (Fig. 4). To map these geo-
metric distortions, we made use of two wire grids (the
so-called “quantum efficiency” and “plasma” grids) that
lie in front of each detector segment. Both grids are
regularly spaced and cover the entire active area of the
detectors. Although designed to be invisible in the spec-
tra, they cast sharp shadows on the detector when il-
luminated directly by on-board stimulation (or “stim”)
lamps. We determined the shifts necessary to straighten
these shadows. Their spacing is approximately 1 mm, too
great to measure fine-scale structure in the X dimension,
but sufficient for the Y distortion. Geometric distortions
in the X dimension have the effect of compressing and
expanding the spectrum in the dispersion direction, so
the X distortion correction is derived in parallel with the
wavelength calibration as described in § 5.1. The geomet-
ric distortion corrections are stored in a set of calibration
files (GEOM CAL) as pairs of 16384× 1024 images, one
each for the X and Y corrections. The value of each im-
6 Dixon et al.
age pixel is the shift necessary to move a photon to its
corrected position. This shift is applied by the subrou-
tine cf geometric distort.
Caveats: Though designed to be invisible, the wire
grids can cast shadows that are visible in the spectra of
astrophysical targets. These shadows are the “worms”
discussed in § 6.3.
4.3.7. Pulse-Height Variations in Detector X Scale
The FUSE detectors convert each ultraviolet photon
into a shower of electrons, for which the detector elec-
tronics calculate the X and Y coordinates and the to-
tal charge, or pulse height. Prolonged exposure to pho-
tons causes the detectors to become less efficient at
this photon-to-electron conversion (a phenomenon called
“gain sag”), and the mean pulse height slowly decreases.
Unfortunately, the X coordinate of low-pulse-height pho-
ton events is systematically miscalculated by the detec-
tor electronics. As the pulse height decreases with time,
spectral features appear to “walk” across the detector.
The strength of the effect depends on the cumulative
photon exposure experienced by each pixel and therefore
varies with location on the detector.
To measure the error in X as a function of pulse height,
we used data from long stim lamp exposures to construct
a series of 32 detector images, each containing events
with a single pulse height (allowed values range from
0 to 31). We stepped through each image in X, com-
puting the shift (∆X) necessary to align the shadow of
each grid wire with the corresponding shadow in a stan-
dard image constructed from photon events with pulse
heights between 16 and 20. The shifts were smoothed
to eliminate discontinuities and stored in calibration files
(PHAX CAL) as a two-dimensional image: the first di-
mension represents the observed X coordinate, and the
second is the pulse height. The value of each image
pixel is the walk correction (∆X) to be added to the
observed value of X. This correction, assumed to be in-
dependent of detector Y position, is applied by the sub-
routine cf pha x distort.
Caveats: For time-tag data, the walk correction is
straightforward and reasonably accurate. For histogram
data, pulse-height information is unavailable, so the
subroutine cf modify hist pha assigns to each photon
event the mean pulse height for that aperture, derived
from contemporaneous time-tag observations and stored
in the calibration file PHAH CAL. While this trick places
time-tag and histogram data on the same overall wave-
length scale, small-scale coordinate errors due to local-
ized regions of gain sag (e.g., around bright airglow lines,
particularly Lyman β) remain uncorrected in histogram
spectra.
4.3.8. Detector Active Region
When the IDF is first created, photon events with co-
ordinates outside the active region of the detector are
flagged as bad (§ 4.2). Once their coordinates are con-
verted to the FARF, the subroutine cf active region
flags as bad any photons that have been repositioned
beyond the active region of the detector. These limits
are read from the electronics calibration file (stored un-
der the header keyword ELEC CAL). Allowed values are
800 ≤ XFARF ≤ 15583, 0 ≤ YFARF ≤ 1023. The
active-area bit is written to the LOC FLGS array.
4.3.9. Uncorrected Detector Effects
CalFUSE does not perform any sort of flat-field correc-
tion. Pre-launch flat-field observations differ sufficiently
from in-flight data to make them unsuitable for this pur-
pose, and in-flight flat-field data are unavailable. (Even
if such data were available, any flat-field correction would
be only approximate, because MCPs do not have physical
pixels for which pixel-to-pixel variations can be clearly
delineated; § 4.3.3). As a result, detector fixed-pattern
noise limits the signal-to-noise ratio achievable in obser-
vations of bright targets. To the extent that grating,
mirror, and spacecraft motions shift the spectrum on the
detector during an exposure, these fixed-pattern features
may be averaged out. For some targets, we deliberately
move the FPAs between exposures to place the spectrum
on different regions of the detector. Combining the ex-
tracted spectra from these exposures can significantly im-
prove the resulting signal-to-noise ratio (§ 4.5.5). Other
detector effects (including the moiré pattern discussed in
§ 6.4) are described in the FUSE Instrument and Data
Handbook.
4.4. Screen Photons
The module cf screen photons calls subroutines de-
signed to identify periods of potentially bad data, such
as Earth limb-angle violations, SAA passages, and de-
tector bursts. A distinct advantage of CalFUSE v3 over
earlier versions of the pipeline is that bad data are not
discarded, but merely flagged, allowing users to mod-
ify their selection criteria without having to re-process
the data. To speed up processing, the pipeline calcu-
lates the various screening parameters once per second
throughout the exposure, sets the corresponding flags in
the STATUS FLAGS array of the timeline table, then
copies the flags from the appropriate entry of the time-
line table into the TIMEFLGS array for each photon
event (§ A-3). Many of the screening parameters applied
by the pipeline are set in the screening parameter file
(SCRN CAL). Other parameters are stored in various
calibration files as described below.
4.4.1. Airglow Events
Numerous geocoronal emission features lie within the
FUSE waveband (Feldman et al. 2001). While the
pipeline processes airglow photons in the same manner
as all other photon events in the target aperture, it is
occasionally helpful to exclude from consideration re-
gions of the detector likely to be contaminated by geo-
coronal or scattered solar emission. These regions are
listed in the calibration file AIRG CAL; the subroutine
cf screen airglow flags as airglow (by setting the air-
glow bit of the LOC FLGS array in the photon-event list)
all photon events falling within the tabulated regions –
even for data obtained during orbital night, when many
airglow features are absent.
4.4.2. Limb Angle
Spectra obtained when a target lies near the earth’s
limb are contaminated by scattered light from strong
geocoronal Lyman α and O I emission. To minimize this
effect, the subroutine cf screen limb angle reads the
LIMB ANGLE array of the timeline table, identifies pe-
riods when the target violates the limb-angle constraint,
CalFUSE: The FUSE Calibration Pipeline 7
and sets the corresponding flag in the STATUS FLAGS
array of the timeline table. Minimum limb angles for day
and night observations are read from the BRITLIMB
and DARKLIMB keywords of the screening parameter
file and copied to the IDF file header. The default limits
are 15◦ during orbital day and 10◦ during orbital night.
4.4.3. SAA Passages
The South Atlantic Anomaly (SAA) marks a depres-
sion in the earth’s magnetic field that allows particles
trapped in the Van Allen belts to reach low altitudes.
The high particle flux in this region raises the background
count rate of the FUSE detectors to unacceptable levels.
The subroutine cf screen saa compares the spacecraft’s
ground track, recorded in the LONGITUDE and LAT-
ITUDE arrays of the timeline table, with the limits of
the SAA (stored in the calibration file SAAC CAL as
a binary table of latitude-longitude pairs) and flags as
bad periods when data were taken within the SAA. Our
SAA model was derived from orbital information and
on-board counter data from the first three years of the
FUSE mission.
Caveats: Because the SAA particle flux is great enough
to damage the FUSE detectors, we end most exposures
before entering the SAA and lower the detector high volt-
age during each SAA pass. As a result, very little data
is actually flagged by this screening step.
4.4.4. Detector High Voltage
The detector high voltage is set independently for each
detector segment (1A, 1B, 2A, 2B). During normal op-
erations, the voltage on each segment alternates between
its nominal full-voltage and a reduced SAA level. The
SAA level is low enough that the detectors are not dam-
aged by the high count rates that result from SAA passes,
and it is often used between science exposures to mini-
mize detector exposure to bright airglow emission. The
full-voltage level is the normal operating voltage used
during science exposures. It is raised regularly to com-
pensate for the effects of detector gain sag. Without
this compensation, the mean pulse height of real photon
events would gradually fall below our detection thresh-
old. Unfortunately, there is a limit above which the
full-voltage level cannot be raised. Detector segment
2A reached this limit in 2003 and has not been raised
since; it will gradually become less sensitive as the frac-
tion of low-pulse-height events increases. The subroutine
cf screen high voltage reads the instantaneous value
of the detector high voltage from the HIGH VOLTAGE
array of the timeline table, compares it with the nominal
full-voltage level (stored as a function of time in the cali-
bration file VOLT CAL), and flags periods of low voltage
as bad.
For any number of reasons, an exposure may be ob-
tained with the detector high voltage at less than the
full-voltage level. To preserve as much of this data as pos-
sible, we examined all of the low-voltage exposures taken
during the first four years of the mission and found that,
for detector segments 1A, 1B, and 2B, the data quality is
good whenever the detector high voltage is greater than
85% of the nominal (time-dependent) full-voltage level.
For segment 2A, data obtained with the high voltage
greater than 90% of full are good, lower than 80% are
bad, and between 80 and 90% are of variable quality. In
this regime, the pipeline flags the affected data as good,
but writes warning messages to both the IDF header and
the trailer file. When this warning is present in time-tag
data, the user should examine the distribution of pulse
heights in the target aperture to ensure that the photon
events are well separated from the background (§ 4.4.12).
For histogram data, the spectral photometry and wave-
length scale are most likely to be affected.
Caveats: If the header keywords indicate that the de-
tector voltage was high, low, or changed during an ex-
posure, the IDF initialization routines (§ 4.2) write a
warning message to the trailer file. If a valid housekeep-
ing file is available for the exposure, this warning may
be safely ignored, because the pipeline uses housekeep-
ing information to populate the HIGH VOLTAGE array
in the timeline table and properly excludes time inter-
vals when the voltage was low. If the housekeeping file
is not present, each entry of the HIGH VOLTAGE array
is set to the “HV bias maximum setting” reported in the
IDF header. In this case, the pipeline has no information
about time-dependent changes in the detector high volt-
age, and warnings about voltage-level changes should be
investigated by the user.
4.4.5. Event Bursts
Occasionally, the FUSE detectors register large count
rates for short periods of time. These event bursts can
occur on one or more detectors and often have a complex
distribution across the detector, including scalloping and
sharp edges (Fig. 5). CalFUSE includes a module that
screens the data to identify and exclude bursts. The sub-
routine cf screen burst computes the time-dependent
count rate using data from background regions of the de-
tector (excluding airglow features) and applies a median
filter to reject time intervals whose count rates differ by
more than 5 standard deviations (the value may be set
by the user) from the mean. The algorithm rejects any
time interval in which the background rate rises rapidly,
as when an exposure extends into an SAA or the tar-
get nears the earth limb. The background rate com-
puted by the burst-rejection algorithm is stored in the
BKGD CNT RATE array of the timeline table and in-
cluded on the count-rate plots generated for each expo-
sure (§ 4.10). Burst rejection is possible only for data
obtained in time-tag mode.
Caveats: Careful examination of long background ob-
servations reveals that many are contaminated by emis-
sion from bursts too faint to trigger the burst-detection
algorithm. Observers studying, for example, diffuse
emission from the interstellar medium should be alert
to the possibility of such contamination.
4.4.6. Spacecraft Drift
Pointing of the FUSE spacecraft was originally con-
trolled with four reaction wheels, which typically main-
tained a pointing accuracy of 0.2–0.3 arc seconds. In late
2001, two of the reaction wheels failed, and it became
necessary to control the spacecraft orientation along one
axis with magnetic torquer bars. The torquer bars can
exert only about 10% of the force produced by the re-
action wheels, and the available force depends on the
strength and relative orientation of the earth’s magnetic
field. Thus, spacecraft drift increased dramatically along
this axis, termed the antisymmetric or A axis. Drifts
8 Dixon et al.
about the A axis shift the spectra of point-source tar-
gets in a direction 45◦ from the dispersion direction (i.e.,
∆X = ∆Y ). These motions can substantially degrade
the resolution of the spectra, so procedures have been
implemented to correct the data for spacecraft motion
during an exposure. For time-tag observations of point
sources, we reposition individual photon events. For his-
togram observations, we correct only for the exposure
time lost to large excursions of the spacecraft. The abil-
ity to correct for spacecraft drift became even more im-
portant when a third reaction wheel failed in 2004 De-
cember.
The correction of photon-event coordinates for space-
craft motion is discussed in § 4.5.7. During screening, the
subroutine cf screen jitter merely flags times when
the target is out of the aperture, defined as those for
which either ∆X or ∆Y , the pointing error in the disper-
sion or cross-dispersion direction, respectively, is greater
than 30′′, the width of the LWRS aperture. These lim-
its, set by the keywords DX MAX and DY MAX in the
CalFUSE parameter file (PARM CAL), underestimate
the time lost to pointing excursions, but smaller limits
can lead to the rejection of good data for some chan-
nels. Also flagged as bad are times when the jitter track-
ing flag TRKFLG = −1, indicating that the spacecraft
is not tracking properly. If TRKFLG = 0, no track-
ing information is available and no times are flagged as
bad. Pointing information is read from the jitter file
(JITR CAL; § A-2). If the jitter file is not present or the
header keyword JIT STAT = 1 (indicating that the jit-
ter file is corrupted), cf screen jitter issues a warning
and exits; again, no times are flagged as bad.
4.4.7. User-Defined Good-Time Intervals
One bit of the status array is reserved for user-defined
GTIs. For example, to extract data corresponding to a
particular phase of a binary star orbit, one would flag
data from all other phases as bad. A number of tools
exist to set this flag, including cf edit (available from
MAST). CalFUSE users may specify good-time inter-
vals by setting the appropriate keywords (NUSERGTI,
GTIBEG01, GTIEND01, etc.) in the screening pa-
rameter file. (Times are in seconds from the exposure
start time.) If these keywords are set, the subroutine
cf set user gtis flags times outside of these good-time
intervals as bad.
4.4.8. Time-Dependent Status Flags
Once the status flags in the timeline table are popu-
lated, the subroutine cf set photon flags copies them
to the corresponding entries in the photon event list. For
time-tag data, this process is straightforward: match the
times and copy the flags. Header keywords in the IDF
record the number of photon events falling in bad time in-
tervals or outside of the detector active area; the number
of seconds lost to bursts, SAAs, etc.; and the remaining
night exposure time. If more than 90% of the exposure
is lost to a single cause, an explanatory note is written
to the trailer file.
The task is more difficult for histogram data, for which
photon-arrival information is unavailable. We distin-
guish between time flags that represent periods of lost
exposure time (low detector voltage or target out of aper-
ture) and those that represent periods of data contami-
nation (limb angle violations or SAAs). For the former,
we need only modify the exposure time; for the latter,
we must flag the exposure as being contaminated. Our
goal is to set the individual photon flags and header key-
words so that the pipeline behaves in the following way:
When processing a single exposure, it treats all photon
events as good. When combining data from multiple
exposures, it excludes contaminated exposures (defined
below). To this end, we generate an 8-bit status word
containing only day/night information: if the exposure
is more than 10% day, the day bit is set. This status
word is copied onto the time-dependent status flag of
each photon event. We generate a second 8-bit status
word containing information about limb-angle violations
and SAAs: if a single second is lost to one of these events,
the corresponding flag is set and a message is written to
the trailer file. (To avoid rejecting an exposure that, for
example, abuts an SAA, we ignore its initial and final 20
seconds in this analysis.) The status word is stored in
the file header keyword EXP STAT (unless that keyword
has already been set; see § 4.2 and § 4.3.1). The routines
used by the pipeline to combine data from multiple ex-
posures into a single spectrum (§ 4.11) reject data files
in which this keyword is non-zero. The number of bad
events, the exposure time lost to periods of low detector
voltage or spacecraft jitter, and the exposure time dur-
ing orbital night are written to the file header, just as for
time-tag data.
Only in this subroutine is the DAYNIGHT keyword
read from the screening parameter file and written to the
IDF file header. Allowed values are DAY, NIGHT, and
BOTH. The default is BOTH. For most flags, if the bit
is set to 1, the photon event is rejected. The day/night
flag is different: it is always 1 for day and 0 for night.
The pipeline must read and interpret the DAYNIGHT
keyword before accepting or rejecting an event based on
the value of its day/night flag.
4.4.9. Good-Time Intervals
Once the time-dependent screening is complete, the
subroutine cf set good time intervals calculates a
new set of good-time intervals from information in the
timeline table and writes them to the second extension
of the IDF (§ 4.2). For time-tag data, all of the TIME-
FLGS bits are used and the DAYNIGHT filter is applied.
For histogram data, the bits corresponding to limb-angle
violations and SAAs are ignored, since data arriving dur-
ing these events cannot be excluded. The DAYNIGHT
filter is applied (assuming that all are day photons if the
exposure is more than 10% day). The exposure time,
EXPTIME = Σ (STOP−START), summed over all en-
tries in the GTI table, is then written to the IDF file
header.
4.4.10. Histogram Arrival Times
For histogram data, all of the photon events in an
IDF are initially assigned an arrival time equal to the
midpoint of the exposure. Should this instant fall in
a bad-time interval, the data may be rejected by a
subsequent step of the pipeline or one of our post-
processing tools. To avoid this possibility, the subroutine
cf modify hist times resets all photon-arrival times to
the midpoint of the exposure’s longest good-time inter-
val. This subroutine is not called for time-tag data.
CalFUSE: The FUSE Calibration Pipeline 9
4.4.11. Bad-Pixel Regions
Images of the FUSE detectors reveal a number of dead
spots that may be surrounded by a bright ring (see the
FUSE Instrument and Data Handbook for examples).
The subroutine cf screen bad pixels reads a list of
bad-pixel regions from a calibration file (QUAL CAL)
and flags as bad all photon events whose XFARF and
YFARF coordinates fall within the tabulated limits. A
bad-pixel map, constructed later in the pipeline (§ 4.8),
is used by the optimal-extraction algorithm to correct for
flux lost to dead spots.
4.4.12. Pulse Height Limits
For time-tag data, the pulse height of each photon
event is recorded in the IDF. Values range from 0 to
31 in arbitrary units. A typical pulse-height distribution
has a peak at low values due to the intrinsic detector
background, a Gaussian-like peak near the middle of the
range due to “real” photons, and a tail of high pulse-
height events, which likely represent the superposition of
two photons and therefore are not reliable. In addition,
the detector electronics selectively discard high pulse-
height events near the top and bottom of the detectors
(i.e., with large or small values of Y). We can thus im-
prove the signal-to-noise ratio of faint targets by rejecting
photon events with extreme pulse-height values. Pulse-
height limits (roughly 2–24) are defined for each detector
segment in the screening parameter file (SCRN CAL).
The subroutine cf screen pulse height flags photon
events with pulse heights outside of this range (by set-
ting the appropriate bit in the LOC FLGS array; § A-3)
and writes the pulse-height limits used and the number
of photon events rejected to the IDF file header. This
procedure is not performed on histogram data.
Caveats: We do not recommend the use of narrow
pulse-height ranges to reduce the detector background
in FUSE data. Careful analysis has shown that limits
more stringent than the default values can result in sig-
nificant flux losses across small regions of the detector,
particularly in the LiF1B channel, resulting in apparent
absorption features that are not real.
4.5. Remove Motions
Having corrected the data for various detector ef-
fects and identified periods of bad data, we continue
to work backwards through the instrument, correcting
for spectral motions on the detector due to the move-
ments of various optical components – and even of the
spacecraft itself. This task is performed by the module
cf remove motions. It begins by reading the XFARF
and YFARF coordinates of each photon event from the
IDF. It concludes by writing the motion-corrected coor-
dinates to the X and Y arrays of the same file.
4.5.1. Locate Spectra on the Detector
The LiF and SiC channels each produce three spec-
tra, one from each aperture, for a total of six spectra
per detector segment (Fig. 3). Because motions of the
optical components can shift these spectra on the detec-
tor, the first step is to determine the Y centroid of each.
To do this, we use the following algorithm: First, we
project the airglow photons onto the Y axis (summing
all values of X for each value of Y) and search the result-
ing histogram for peaks within 70 pixels of the expected
Y position of the LWRS spectrum. If the airglow fea-
ture is sufficiently bright (33 counts in 141 Y pixels), we
adopt its centroid as the airglow centroid for the LWRS
aperture and compute its offset from the expected value
stored in the CHID CAL calibration file. If the airglow
feature is too faint, we adopt the expected centroid and
assume an offset of zero. We apply the offset to the ex-
pected centroids of the MDRS and HIRS apertures to
obtain their airglow centroids. Second, we project the
non-airglow photons onto the Y axis and subtract a uni-
form background. Airglow features fill the aperture, but
point-source spectra are considerably narrower in Y and
need not be centered in the aperture. For each aperture,
we search for a 5σ peak within 40 pixels of the airglow
centroid. If we find it, we use its centroid; otherwise,
we use the airglow centroid. This scheme, implemented
in the subroutine cf find spectra, allows for the pos-
sibility that an astrophysical spectrum may appear in a
non-target aperture.
For each of the six spectra, two keywords are written
to the IDF file header: YCENT contains the computed
Y centroid, and YQUAL contains a quality flag. The
flag is HIGH if the centroid was computed from a point-
source spectrum, MEDIUM if computed from an airglow
spectrum, and LOW if the tabulated centroid was used.
It is possible for the user to specify the target centroid
by setting the SPEX SIC and SPEC LIF keywords in
the CalFUSE parameter file (PARM CAL). Two other
keywords, EMAX SIC and EMAX LIF, limit the offset
between the expected and calculated centroids: if the cal-
culated centroid differs from the predicted value by more
than this limit, the pipeline uses the default centroid.
Caveats: On detector 1, the SiC LWRS spectrum falls
near the bottom edge of the detector (Fig. 3). Because
the background level rises steeply near this edge, the cal-
culated centroid can be pulled (incorrectly) to lower val-
ues of Y, especially for faint targets.
4.5.2. Assign Photons to Channels
The subroutine cf identify channel assigns each
photon to a channel, where “channel” now refers to one
of the six spectra on each detector (Fig. 3). For each
channel, extraction windows for both point-source and
extended targets are tabulated in the calibration file
CHID CAL along with their corresponding spectral Y
centroids. These extraction limits encompass at least
99.5% of the target flux. For the target channels, iden-
tified in the APERTURE header keyword, we use either
the point-source or extended extraction windows, as indi-
cated by the SRC TYPE keyword; for the other (presum-
ably airglow) channels, we use the extended extraction
windows. The offset between the calculated and tabu-
lated spectral Y centroids (§ 4.5.1) is used to shift each
extraction window to match the data.
To insure that, should two extraction windows overlap,
photon events falling in the overlap region are assigned
to the more likely channel, photon coordinates (XFARF
and YFARF) are compared with the extraction limits
of the six spectral channels in the following order: first
the target channels (LiF and SiC); then the airglow chan-
nels (LiF and SiC) corresponding to the larger non-target
aperture; and finally the airglow channels (LiF and SiC)
10 Dixon et al.
corresponding to the smaller non-target aperture. For
example, if the target were in the MDRS aperture, the
search order would be MDRS LiF, MDRS SiC, LWRS
LiF, LWRS SiC, HIRS LiF, and HIRS SiC. The process
stops when a match is made. The channel assignment
of each photon event is stored in the CHANNEL array
(§ A-3); photon events that do not fall in an extraction
window are assigned a CHANNEL value of 0.
Channel assignment is performed twice, once before
the motion corrections and once after. The first time, all
extraction windows are padded by ±10 Y pixels to ac-
commodate errors in the channel centroids. The second
time, no padding is applied to time-tag data. Histogram
data, which are generally binned by 8 pixels in Y, present
a special challenge: The geometric correction (§ 4.3.6)
can move a row of data out of the extraction window,
producing a significant loss of flux. To prevent this, his-
togram extraction windows are padded by an additional
±8 Y pixels (or an amount equal to the binning factor
in Y, if other than 8).
4.5.3. Track Y Centroids with Time
For the LiF and SiC target apertures, the subroutine
cf calculate ycent motion computes the spectral Y
centroid as a function of time throughout the exposure.
The algorithm requires 500 photon events to compute
an average, so the centroid is updated more often for
bright targets than for faint ones. Photon events flagged
as airglow are ignored. The results are stored in the
YCENT LIF and YCENT SIC arrays of the timeline ta-
ble, but are not currently used by the pipeline. This cal-
culation is not performed for data obtained in histogram
mode.
4.5.4. Correct for Grating Motion
The FUSE spectrograph gratings are subject to small,
thermally-induced motions on orbital, diurnal, and pre-
cessional (60-day) timescales; an additional long-term,
non-periodic drift is also apparent. These motions can
shift the target and airglow spectra by as much as 15
pixels (peak to peak) in both the X and Y dimensions.
Measurements of the Lyman β airglow line in thousands
of exposures obtained throughout the mission reveal that
the gratings’ orbital motion depends on three parame-
ters: beta angle (the angle between the target and the
anti-sun vector), pole angle (the angle between the tar-
get and the orbit pole), and spacecraft roll angle (east of
north, stored in the file-header keyword APER PA). The
subroutine cf grating motion compares the beta, pole,
and roll angles of the spacecraft with a grid of values
in the calibration file GRAT CAL, reads the appropri-
ate correction, and computes the X and Y photon shifts.
The grating-motion correction is applied to all photon
events with CHANNEL > 0; photon events not assigned
to a channel are not moved.
Caveats: Some combinations of beta and pole angle
are too poorly sampled for us to derive a grating-motion
correction; for these regions, no correction is applied. At
present, only corrections for the orbital and long-term
grating motions are available. Because all photon events
in histogram data are assigned the same arrival time (the
midpoint of the longest good-time interval), they receive
the same grating-motion correction.
4.5.5. Correct for FPA Motion
The four focal-plane assemblies (shown in Fig. 1) can
be moved independently in either the X or Z direction.
FPA motions in the X direction are used to correct
for mirror misalignments and to perform FP splits (de-
scribed below). FPA motions in the Z direction are used
to place the apertures in the focal plane of the spectro-
graph. (Strictly speaking, an FPA moves along the tan-
gent to or the radius of the spectrograph Rowland circle,
not the X and Z axes shown in Fig. 1.) Both motions
change the spectrograph entrance angle, shifting the tar-
get spectrum on the detector. The FUSE wavelength
calibration is derived from a single stellar observation
obtained at a particular FPA position. The subroutine
cf fpa position computes the shift in pixels (∆X) nec-
essary to move each spectrum from its observed X posi-
tion on the detector to that of the wavelength-calibration
target. The X and Z positions of the LiF and SiC FPAs
are stored in file header keywords, various spectrograph
parameters are stored in a calibration file (SPEC CAL),
and the wavelength calibration and the FPA position
of the wavelength-calibration target are stored in the
WAVE CAL file. Shifts are computed for both the LiF
and SiC channels; the appropriate shift is applied to all
photon events with CHANNEL > 0; photon events not
assigned to a channel are ignored.
The FUSE detectors suffer from fixed-pattern noise.
Astigmatism in the instrument spreads a typical resolu-
tion element over several hundred detector pixels (pre-
dominantly in the cross-dispersion dimension), mitigat-
ing this effect, but to achieve a signal-to-noise ratio
greater than ∼ 30, one must remove the remaining fixed-
pattern noise. A useful technique is the focal-plane split.
FP splits are performed by obtaining a series of MDRS
or HIRS exposures at several FPA X positions. Moving
the FPA in the X dimension (and moving the satellite
to center the target in the aperture) between exposures
shifts both target and airglow spectra in the dispersion
direction on the detector. CalFUSE shifts each spectrum
to the standard X position expected by our wavelength
calibration routines. If the signal-to-noise ratio in the
spectra obtained at each FPA position is high enough, it
is possible for the user to disentangle the source spectrum
from the detector fixed-pattern noise; however, simply
combining extracted spectra obtained at different FPA
positions will average out most of the small-scale detec-
tor features.
4.5.6. Correct for Mirror Motion
The spectrograph mirrors are subject to thermal mo-
tions that shift the target’s image within the spectro-
graph aperture and thus its spectrum in both X and Y
on the detector. A source in either of the SiC channels
may move as much as 6′′ in a period of 2 ks. This motion
has two effects on the data: first, flux will be lost if the
source drifts (partially or completely) out of the aperture;
second, spectral resolution will be degraded (for LWRS
observations) as the spectrum shifts on the detector. Dif-
fuse sources, such as airglow emission, fill the aperture,
so their spectra are unaffected by mirror motion.
When the LiF1 channel is used for guiding, motions
of the LiF1 mirror are corrected by the spacecraft it-
self. Only the LiF2 and SiC spectra must be corrected
CalFUSE: The FUSE Calibration Pipeline 11
by CalFUSE. In theory, the switch from LiF1 to LiF2
as the primary channel for guiding the spacecraft (§ 2)
should require another set of calibration files. In practice,
the LiF2 mirror motion in the dispersion direction tracks
that of the LiF1 mirror. The mirror-motion correction is
stored as a function of time since orbital sunset (via the
TIME SUNSET array in the timeline table) in the cali-
bration file MIRR CAL. The correction (∆X) is applied
by the subroutine cf mirror motion to all photon events
within the target aperture; photon events in other aper-
tures and those not assigned to a channel are ignored.
This correction is not applied to extended sources. Be-
cause all photon events in histogram data are assigned
the same arrival time (generally the midpoint of the ex-
posure), they receive the same mirror-motion correction.
Caveats: We correct only the relative mirror motion
during an orbit, not the absolute mirror offset based on
longer-term trends. We do not correct for mirror motions
in the Y dimension. Finally, because the shifts for the
SiC1 and SiC2 mirrors are similar, we adopt a single
correction for both channels.
4.5.7. Correct for Spacecraft Motion
Spacecraft motions during an exposure shift the tar-
get spectrum on the detector and thus degrade spectral
resolution. The subroutine cf satellite jitter uses
pointing information stored in the jitter file (JITR CAL;
§ A-2) to correct the observed coordinates of photon
events for these motions. Pointing errors in arc sec-
onds are converted to X and Y pixel shifts and applied
to all photon events within the target aperture; events
in other apertures and those not assigned to a channel
are ignored. The correction is applied only if the jitter
tracking flag TRKFLG > 0, indicating that valid track-
ing information is available. TRKFLG values rise from
1 to 5 as the reliability of the pointing information in-
creases. The minimum acceptable value of the TRKFLG
may be adjusted by modifying the TRKFLG keyword in
the CalFUSE parameter file (PARM CAL).
4.5.8. Recompute Spectral Centroids
Once all spectral motions are removed from the data,
the subroutine cf calculate y centroid recomputes
the spectral Y centroids. Separate source and airglow
centroids are determined for each aperture in turn, from
largest to smallest. (The former is meaningless if the
aperture does not contain a source.) The offset be-
tween the measured airglow centroid in the LWRS aper-
ture and the tabulated centroid (from the calibration file
CHID CAL) is used to compute the airglow centroids for
the MDRS and HIRS apertures; the computed MDRS
and HIRS airglow centroids are ignored. The YCENT
value written to the IDF file header is determined by
the quality flag previously set by cf find spectra (§
4.5.1): if YQUAL = HIGH, the source centroid is used;
if YQUAL = MEDIUM, the airglow centroid is used; and
if YQUAL = LOW, the tabulated centroid is used. The
SPEX SIC, SPEC LIF, EMAX SIC, and EMAX LIF
keywords in the CalFUSE parameter file (PARM CAL)
have the effects discussed in § 4.5.1.
4.5.9. Final Assignment of Photons to Channels
The final assignment of each photon event to a chan-
nel is performed by cf identify channel, just as in §
4.5.2, but with two modifications: First, we consider
only photon events with CHANNEL > 0; unassigned
events (which are not motion corrected) remain unas-
signed. Second, we do not pad the extraction windows
by an additional ±10 pixels in Y, though the extraction
windows for histogram data are padded by ±8 Y pixels
(or an amount equal to the binning factor in Y, if other
than 8), as before.
4.5.10. Compute Count Rates
For time-tag data, cf target count rate computes
the count rate in the target aperture for the LiF and
SiC channels. To account for dead-time effects, the con-
tents of the WEIGHT array are used. Events in airglow
regions are excluded, but no other filters are applied to
the data. Results are written to the LIF CNT RATE
and SIC CNT RATE arrays of the timeline table. For
histogram data, the initial values of these arrays, taken
from the housekeeping file (§ A-3), are scaled by the value
of the header keyword DET DEAD.
4.6. Wavelength Calibration
Once converted to the FARF and corrected for optical
and spacecraft drifts, the data can be wavelength cal-
ibrated. The module cf assign wavelength performs
three tasks: first, it corrects for astigmatism in the spec-
trograph optics; second, it applies a wavelength calibra-
tion to each photon event; and third, it shifts the wave-
lengths to a heliocentric reference frame. The derivation
of the FUSE wavelength scale is discussed in § 5.1.
4.6.1. Astigmatism Correction
The astigmatic height of FUSE spectra perpendicular
to the dispersion axis is significant and varies as a func-
tion of wavelength (Fig. 3). Moreover, spectral features
show considerable curvature, especially near the ends of
the detectors where the astigmatism is greatest. The
subroutine cf astigmatism shifts each photon event in
X to correct for the spectral-line curvature introduced by
the FUSE optics, providing a noticeable improvement in
spectral resolution for point sources (Fig. 6).
The astigmatism correction is derived from observa-
tions of GCRV 12336, the central star of the Dumbbell
Nebula (M 27), which exhibits H2 absorption features
across the FUSE waveband. We cross-correlate and com-
bine the absorption features from a small range in X, fit
a parabola to each set of combined features, compute the
shift required to straighten each parabola, and interpo-
late the shifts across the waveband. Because an astigma-
tism correction has been derived only for point sources,
no correction is performed on the spectra of extended
sources, airglow spectra, or observations with APER-
TURE = RFPT.
The correction is stored in the calibration file
ASTG CAL as a two-dimensional image representing the
region of the detector containing the target spectrum. A
separate image is provided for each aperture. The value
of each image pixel is the astigmatism correction (∆X
in pixels) to be applied to that pixel. The entire image
is shifted in Y to match the centroid of the target spec-
trum, and the appropriate correction is applied to each
photon event in the target aperture. The corrected X co-
ordinates are not written to the IDF, but are passed im-
mediately to the wavelength-assignment subroutine. In
12 Dixon et al.
effect, we apply a two-dimensional wavelength calibra-
tion, which depends upon both the X and Y coordinates
of each photon event.
4.6.2. Assign Wavelengths
The wavelength calibration is stored as a binary table
extension in the calibration file WAVE CAL (§ 5.1); a
separate extension is provided for each aperture. Wave-
lengths are tabulated for integral values of X, assumed
to be in motion-corrected FARF coordinates. Given
the astigmatism-corrected X and CHANNEL arrays, the
subroutine cf dispersion considers each aperture in
turn and reads the corresponding calibration table. It
interpolates between tabulated values of X to derive the
wavelength of each photon event. Photon events not as-
signed to an aperture (CHANNEL = 0) are not wave-
length calibrated.
4.6.3. Doppler Correction
The component of the spacecraft’s orbital velocity
in the direction of the target is stored in the OR-
BITAL VEL array of the timeline table. The component
of the earth’s orbital velocity in the direction of the tar-
get is stored in the IDF file header keyword V HELIO.
Their sum is used to compute a time-dependent Doppler
correction, which is applied to each photon event by the
subroutine cf doppler and heliocentric. The result-
ing wavelength scale is heliocentric. Because histogram
data are assigned identical arrival times, their Doppler
correction is not time dependent. Histogram exposures
are kept short (approximately 500 seconds) to minimize
the resulting loss of spectral resolution. The final wave-
length assigned to each photon event is stored in the
LAMBDA array of the IDF.
4.7. Flux Calibration
Because the instrument sensitivity varies through the
mission, we employ a set of time-dependent effective-
area files (AEFF CAL). We interpolate between the two
files whose dates bracket the exposure start time but
do not extrapolate beyond the most recent effective-area
file. Within each calibration file, the instrumental ef-
fective area is stored as a binary table extension, with
a separate extension provided for each aperture. The
module cf flux calibrate invokes a single subroutine,
cf convert to ergs. Considering each aperture in turn,
the program reads the appropriate calibration files, inter-
polates between them if appropriate, and computes the
“flux density” of each photon (in units of erg cm−2) ac-
cording to the formula
ERGCM2 = WEIGHT× hc / LAMBDA / Aeff(λ), (1)
where ERGCM2, WEIGHT, and LAMBDA are read
from the photon-event list (§ A-3), h is Planck’s con-
stant, c the speed of light, and Aeff(λ) the effective area
at the wavelength of interest. Only photon events as-
signed to an aperture are flux calibrated; events with
CHANNEL = 0 are ignored. The flux density computed
for each photon event is stored in the ERGCM2 array of
the IDF. This array is not used by the pipeline, but is
employed by some of our interactive IDF manipulation
tools.
4.8. Create Bad-Pixel Map
When possible, spectra are extracted using an optimal-
extraction algorithm (§ 4.9.4) that employs a bad-pixel
map (BPM) to correct for flux lost to dead spots and
other detector blemishes. Because motions of the space-
craft and its optical components cause FUSE spectra
to wander on the detector, a particular spectral feature
may be affected by a dead spot for only a fraction of an
exposure. We thus generate a bad-pixel map for each
exposure. The module cf bad pixels reads a list of
dead spots from a calibration file (QUAL CAL), deter-
mines which of them overlap the target aperture, tracks
the motion corrections applied to each, and converts the
motion-corrected coordinates to wavelengths. The re-
sulting bad-pixel map (identified by the suffix “bpm.fit”)
has a format similar to that of an IDF (§ A-4), but the
WEIGHT column, whose values range from 0 to 1, rep-
resents the fraction of the exposure that each pixel was
affected by a dead spot. No BPM file is created if the
(screened) exposure time is less than 1 second. BPM files
are not archived, but can be generated from an IDF and
its associated jitter file using software distributed with
CalFUSE (available from MAST).
4.9. Extract Spectra
Through all previous steps of the pipeline, we resist
the temptation to convert the photon-event list into an
image. In the module cf extract spectra, we relent.
Indeed, we generate four sets of images: a background
model, a bad-pixel mask, a two-dimensional probabil-
ity distribution of the target flux, and a spectral image
for each extracted spectrum (LiF and SiC). Only pho-
ton events that pass all of the requested screening steps
(§ 4.4) are considered. If the (screened) exposure time
is less than 1 second or no photon events survive the
screening routines, then the program generates a null-
valued spectral file.
4.9.1. Background Model
Microchannel plates contribute to the detector back-
ground via beta decay of 40K in the MCP glass. On
orbit, cosmic rays add to this intrinsic background to
yield a total rate of ∼ 0.5 counts cm−2 s−1. Scattered
light, primarily geocoronal Lyman α, contributes a well-
defined illumination pattern (Fig. 7) that varies in inten-
sity during the orbit, with detector-averaged count rates
as small as 20% of the intrinsic background during the
night and 1-3 times the intrinsic rate during the day. We
assume that the observed background consists of three
independent components, a spatially uniform dark count
and spatially-varying day- and nighttime scattered-light
components. Properly scaling them to the data is thus a
problem with three unknown parameters. We attempt to
fit as many of these parameters as possible directly from
the data. When such a fit is not possible, we estimate
one or more components and fit the remainder.
Background events due to the detector generally have
pulse-height values lower than those of real photons (§
4.4.12). The observed dark count thus depends on the
pulse-height limits imposed on the data. An initial esti-
mate of the dark count, as a function of the lower pulse-
height threshold, is read from the background characteri-
zation file (BCHR CAL). The day and night components
CalFUSE: The FUSE Calibration Pipeline 13
of the scattered-light model are read from separate exten-
sions of the appropriate time-dependent background cal-
ibration file (BKGD CAL). The background models are
scaled to match the counts observed on unilluminated
regions of the detector. The Y limits of these regions
(selected according to the target aperture) are read from
header keywords in the IDF. Airglow photons in these
regions are excluded from the analysis. The day and
night counts in the background regions of the detector
are summed and recorded.
In its default mode, the subroutine cf scale bkgd es-
timates the uniform background as follows: The back-
ground regions of the day and night scattered-light mod-
els are scaled by their relative exposure times, summed,
and projected onto the X axis (to produce a histogram
in X). A similar histogram (called the “empirical back-
ground spectrum”) is constructed from the data. An it-
erative process is used to determine the uniform compo-
nent and scattered-light scale factor that best reproduce
the observed X distribution of background counts. The
uniform component is then subtracted from the day and
night totals computed above, and the day and night com-
ponents of the scattered-light model are scaled to match
the remaining observed counts.
If the empirical background spectrum is too faint, we
do not attempt to fit it, but assume that the uniform
component of the background is equal to the tabulated
dark-count rate scaled by the exposure time. The day
and night components of the scattered-light model are
calculated as above. Users who require a more accurate
background model may wish to combine data from mul-
tiple exposures before extracting a spectrum (see § 6.6).
If the empirical background spectrum is very bright –
as, for example, when nebular emission or a background
star contaminates one of the other apertures (and thus
the background-sample region) – no fit is performed. In-
stead, the day and night components of the scattered-
light model are scaled by the day and night exposure
times, the tabulated dark-count rate is scaled by the to-
tal exposure time, and the three components are summed
to produce a background image. This scheme is also used
for histogram data. Because histogram files contain data
only from the region about the desired extraction win-
dow, the background cannot be estimated from other
regions of the detector. Fortunately, histogram observa-
tions typically consist of short exposures of bright tar-
gets, so the background is comparatively faint.
Our day and night scattered-light models were de-
rived from the sum of many deep background observa-
tions spanning hundreds of kiloseconds. Individual ex-
posures that differ markedly from the mean were ex-
cluded from the sum. The data were processed only
through the FARF-conversion and data-screening steps
of the pipeline. Airglow features were replaced with a
mean background interpolated along the dispersion (X)
axis of the detector. An estimate of the uniform back-
ground component was subtracted from the final image,
and the data were binned by 16 detector pixels in X. This
process was performed on both day- and night-only data
sets. We produce a new set of background images every 6
to 12 months, as the effects of gain sag and adjustments
of the detector high voltage slowly alter the relative sen-
sitivity of the illuminated and background regions of the
detectors.
Caveats: While early versions of the pipeline (through
v2.4) assume a 10% uncertainty in the background flux,
propagating it through to the final extracted spectrum,
CalFUSE v3 treats uncertainties in the background as
systematic errors and does not include them in the
(purely statistical) error bars of the extracted spectra.
The algorithm assumes that the intensity of the uni-
form background is constant throughout an exposure.
This would be the case if it were due only to the detec-
tor dark count, but in fact the uniform background in-
cludes a substantial contribution from the scattered light
and is thus brighter during day-time portions of an expo-
sure. The assumption of a constant uniform background
can lead the algorithm to over-estimate the scale factors
for both the uniform and spatially-varying components
of the background model. A better scheme would be to
fit the day and night components of the uniform back-
ground separately. Similarly, when the empirical back-
ground spectrum is very faint, adopting the tabulated
value of the uniform background is not the best solu-
tion. It is likely that the scattered-light component of
the uniform background would be better estimated from
the observed day and night backgrounds. The difference
between the tabulated and observed levels of the uniform
background will become greater as the mission extends
into solar minimum and the intensities of both individ-
ual airglow features and the scattered-light component
of the uniform background continue to weaken.
Grating scattering of point-source photons along the
dispersion direction is potentially significant and is not
corrected by the CalFUSE pipeline. Typical values are
1–1.5% of the continuum flux in the SiC channels and 10
times less in the LiF channels.
4.9.2. Probability Array
The optimal-extraction algorithm (§ 4.9.4) requires as
input a two-dimensional probability array representing
the distribution of flux on the detector. Separate prob-
ability arrays, derived from high signal-to-noise stellar
observations, have been computed for each channel and
stored as image extensions in the weights calibration file
(WGTS CAL).
By construction, the Y dimension of the probability
array represents the maximum extent of the extraction
window for a particular aperture. For simplicity, all ar-
rays employed by the optimal-extraction algorithm are
trimmed to match the probability array in Y. The cen-
troids of the probability distribution and the target spec-
trum (recorded in the corresponding file headers) are
used to determine the offset between detector and prob-
ability coordinates. In the X dimension, all arrays are
binned to the output wavelength scale requested by the
user. Default wavelength parameters for each aperture
are specified in the header of the wavelength calibration
file (WAVE CAL); the default binning for all channels is
0.013 Å per output spectral bin, which corresponds to
approximately two detector pixels. The background ar-
ray, originally binned by 16 pixels in X, is rescaled to the
width of each output wavelength bin by the subroutine
cf rebin background. The probability array is rescaled
by the subroutine cf rebin probability array to have
a sum of unity in the Y dimension for each wavelength
14 Dixon et al.
4.9.3. Bad-Pixel Mask
A bad-pixel mask with the same wavelength scale and
Y dimensions as the probability array is constructed from
the BPM file (§ 4.8) by the subroutine cf make mask.
The array is initialized to zero. For each entry in the
BPM file, the value of the WEIGHT array is added to
the corresponding pixel of the bad-pixel mask. The mask
is then normalized and inverted, so that the center of the
deepest dead spot has a value of 0 and the regions outside
are set to 1. The conversion from pixel to wavelength
coordinates can open gaps in the mask, which appear as
values of unity surrounded by pixels with lower values.
We search for array elements that are larger than their
neighbors and replace them with the mean value of the
adjoining pixels. If the BPM file is absent or a particular
aperture is free of bad pixels, all elements of the bad-pixel
mask are set to 1.
4.9.4. Optimal (Weighted) Spectral Extraction
The extraction subroutine, cf optimal extraction,
is called separately for each of the two target spectra
(LiF and SiC). Inputs include the photon-event list and
the indices of events that pass through the various screen-
ings, as well as the 2-D background, probability, and bad-
pixel arrays described above. For numerical simplicity,
extraction is performed using the WEIGHT of each pho-
ton event, rather than its ERGCM2 value. A pair of 2-D
data and variance arrays with the same dimensions as the
probability array are constructed from the good photons
whose CHANNEL values correspond to the target aper-
ture. For time-tag data, this process is straightforward:
the LAMBDA and Y values of each photon event corre-
spond to a particular cell in the data and variance arrays.
That cell in the data array is incremented by the photon
weight, while the corresponding cell in the variance array
is incremented by the square of the weight. A 1-D raw-
counts spectrum (useful for the statistical analysis of low
count-rate data) is constructed simultaneously: for each
photon event added to the data array, the appropriate
bin of the counts spectrum is incremented by one.
For histogram data, the process is more complex, be-
cause the original detector image is generally binned by
8 pixels in Y and because each entry in the photon-event
list represents the sum of many individual photons. In
the Y dimension, an event’s WEIGHT is divided among
8 pixels (or the actual Y binning for that exposure, if
different) according to the distribution predicted by the
probability array. In the X dimension, each event is as-
sumed to have a width in wavelength space equal to the
mean dispersion per pixel for the channel (read from the
DISPAPIX keyword of the WAVE CAL file), and the
WEIGHT of an event that spans the boundary between
two output wavelength bins is divided between them.
This smoothing in X helps to mitigate the “beating” that
would otherwise occur between detector pixels and out-
put wavelength bins.
One-dimensional background, weights, and variance
spectra are then extracted from the two-dimensional
background, data, and variance arrays. To insure that
the three spectra sample the same region of the detector,
only cells in the 2-D arrays for which the correspond-
ing cell in the probability array has a value greater than
10−4 are included in the sum. These limits differ slightly
from those defined in the aperture (CHID CAL) calibra-
tion files. As a result, the ratio of the final weights and
counts spectra may not be a constant. (Ideally, their ra-
tio would equal the mean dead-time correction for the
exposure.) An initial flux spectrum, equal to the differ-
ence of the weights and background spectra, is used as
input to the optimal-extraction algorithm.
CalFUSE employs the optimal-extraction algorithm
described by Horne (1986), which requires as input the
2-D data, background, probability, and bad-pixel arrays
and the 1-D initial flux spectrum. Originally designed for
CCD spectroscopy, the algorithm has been modified for
the FUSE detectors. Specifically, instead of constructing
a 2-D spatial profile from each data set, we use a tabu-
lated probability array; the 2-D cosmic-ray mask, an in-
teger array in the original algorithm, is replaced with the
bad-pixel mask, which is a floating-point array; and the
2-D variance estimate is scaled by the bad-pixel mask.
Extraction is iterative: in the original version, iteration
is performed until the cosmic-ray mask stops changing.
In our version, iteration continues until the output flux
spectrum changes by less than 0.01 counts in all pixels. If
the loop repeats 50 times, the algorithm fails. The num-
ber of iterations performed is written to the OPT EXTR
keyword of the output file header.
If optimal extraction is successful, the variance of
the optimal spectrum is computed using the recipe of
Horne (1986). We have adapted this recipe to pro-
duce weights and background spectra such that FLUX
= WEIGHTS − BKGD. The resulting background spec-
trum is not smooth. Optimal extraction is not per-
formed on the spectra of extended sources or on those
for which the quality of the computed spectral centroid
is not HIGH. (Both the centroid and its quality flag are
stored in file header keywords.) In these cases, or if the
optimal-extraction algorithm fails, the initial flux, vari-
ance, weights, and background spectra are adopted.
However they are constructed, the final FLUX, ER-
ROR (equal to the square root of the variance),
WEIGHTS, and BKGD arrays (all in units of counts)
are returned to the calling routine. For time-tag data,
the COUNTS array as described above is returned. For
histogram data, the COUNTS array is computed by di-
viding the final WEIGHTS array by the mean dead-
time correction, which is stored in a file-header keyword
TOT DEAD. Also returned is the QUALITY array. It
is the product of the probability array and the bad-pixel
map, projected onto the wavelength axis and expressed
as an integer between 0 and 100. Its value is 0 if all the
flux in a wavelength bin is lost to a detector dead spot,
100 if no flux is lost.
Caveats: The optimal-extraction algorithm is designed
to improve the signal-to-noise ratio of the spectra of faint
point sources. Unfortunately, it is in precisely these cases
that the spectral centroid is most likely to be uncertain.
Because proper positioning of the probability array is es-
sential to the weighting scheme, observers of faint targets
may wish to combine the IDFs from multiple exposures
and re-compute their spectral centroids before attempt-
ing optimal extraction.
4.9.5. Extracted Spectral Files
Because the optimal-extraction routine returns the fi-
nal FLUX and ERROR arrays in units of counts, the
CalFUSE: The FUSE Calibration Pipeline 15
spectral-extraction module applies a flux calibration to
both arrays using the subroutine cf convert to ergs
described in § 4.7. Dividing each array element by (EX-
PTIME × WPC), where EXPTIME is the length in sec-
onds of the (screened) exposure and WPC the width in
Ångstroms of each output wavelength bin, completes the
conversion to units of erg cm−2 s−1 Å−1. The format of
the extracted spectral files is described in § A-5.
4.10. Trailer and Image Files
A number of supplementary files are generated by Cal-
FUSE and archived with the data. For each exposure
and detector segment, the pipeline generates a trailer file
and a pair of image files in Graphics Interchange Format
(GIF). The trailer file (suffix “.trl”) contains timing in-
formation for all pipeline modules and any warning or
error messages that they may have generated. The first
image file contains an image of the detector overlaid by a
wavelength scale and extraction windows for each aper-
ture (suffix “ext.gif”). Only photon events flagged as
good are included in the plot, unless there are none, in
which case all events are plotted. The second image file
presents count-rate plots for both the LiF and SiC target
apertures (suffix “rat.gif”). These arrays come from the
timeline table in the IDF and exclude photons flagged as
airglow. These image files are powerful tools for diagnos-
ing problems in the data, revealing, for example, when
high background levels cause the SiC1 LWRS extraction
window to be misplaced.
4.11. Observation-Level Files
For each exposure, CalFUSE produces LiF and SiC
spectra from each of four detector segments, for a total
of eight extracted spectral files. OPUS combines them
into a set of three observation-level files for submission
to MAST. Observation-level files are distinguished from
exposure-level files by having an exposure number of 000.
Depending on the target and the scientific questions at
hand, these files may be of sufficient fidelity for scientific
investigation. Here is a brief description of their contents:
ALL: For each combination of detector segment and
channel (LiF1A, SiC1A, etc.), we combine data from all
exposures in the observation into a single spectrum. If
the individual spectra are bright enough, we cross corre-
late and shift them before combining. (For each channel,
the shift calculated for the detector segment spanning
1000–1100 Å is applied to the other segment as well.) If
the spectra are too faint for cross correlation, we com-
bine the individual IDFs and extract a single spectrum
to optimize the background model. Combined spectra
(WAVE, FLUX, and ERROR arrays) for each of the eight
channels are stored in separate binary table extensions
in the following order: 1ALIF, 1BLIF, 2BLIF, 2ALIF,
1ASIC, 1BSIC, 2BSIC, and 2ASIC.
ANO (all, night-only): With the same format as the
ALL files, these spectra are constructed using only data
obtained during the night-time portion of each exposure.
They are generated only for time-tag data, and only if
EXPNIGHT > 0. The shifts calculated for the ALL files
are applied to the night-only data; they are not recom-
puted.
NVO (National Virtual Observatory): These files con-
tain a single spectrum spanning the entire FUSE wave-
length range. The spectrum is assembled by cutting and
pasting segments from the most sensitive channel at each
wavelength. Segments are shifted to match the guide
channel (either LiF1 or LiF2) between 1045 and 1070 Å.
Columns are WAVE, FLUX, and ERROR and are stored
in a single binary table extension.
The ALL file is used to generate a “quick-look” spec-
tral plot for each observation. When available, combined
spectra from channels spanning the FUSE waveband are
plotted in a single GIF image file (suffix “specttagf.gif”
or “spechistf.gif”). This plot appears on the MAST pre-
view page of each observation. Four additional GIF files
contain the combined LiF1, LiF2, SiC1, and SiC2 spectra
for each observation.
Caveats: Cross-correlation may fail, even for the spec-
tra of bright stars, if they lack strong spectral features.
Examples are nearby white dwarfs with weak interstellar
absorption lines. If cross-correlation fails for a given ex-
posure, that exposure is excluded from the sum. Thus,
the exposure time for a particular segment in an ALL
file may be less than the total exposure time for that
observation.
The cataloging software used by MAST requires the
presence of an ALL file for each exposure, not just for
the entire observation. We generate exposure-level ALL
files, but they contain no data, only a FITS file header.
The observation-level ALL files discussed above can be
distinguished by the string “00000all” in their names.
4.12. Quality Control and Archiving
Before the reduced data are archived, they undergo
a two-step quality-control process: First, a set of auto-
mated checks is performed on each exposure. The soft-
ware compares the flux observed in the guide channel
(LiF1 or LiF2) with that expected for the target and
with that observed in the other three channels. If an
anomaly is detected, a flag is set requesting manual in-
vestigation. The software works well for bright contin-
uum sources, but often flags faint or emission-line targets
as unsuccessful observations. Second, a member of the
FUSE operations team investigates any warnings gener-
ated by the software. If it is determined that less than
50% of the requested data were obtained, the target is
re-observed.
The philosophy of the FUSE project is to archive data
whenever possible, even if it does not satisfy the require-
ments of the original investigator. As a result, the MAST
archive contains a number of FUSE data sets that are in
some way flawed (e.g., misaligned channels, partial loss
of guiding, or no good observing time). Users should be
aware of this possibility.
If the pipeline detects an error in the data or its as-
sociated housekeeping or jitter files, it writes a warning
to both the trailer file and the headers of the IDF and
extracted spectral files. Users are advised to scan trailer
files for the “WARNING” string and spectral files for
“COMMENT” records. Occasionally, the FUSE opera-
tions team inserts comments directly into the headers of
raw data files. Such comments may warn of an unusual
instrument configuration, errors in the reported target
coordinates, or data obtained during slews.
Observation names beginning with the letter “S” are
science-verification observations and may have been ob-
tained with an unusual instrument configuration. For
16 Dixon et al.
example, the program S523 was designed to test pro-
cedures for observing a bright object by defocusing the
SiC mirrors. The LiF mirrors were moved for some S523
exposures. As a result, data from this program should
be used with caution. Abstracts for all FUSE observing
programs are available from MAST.
5. CALIBRATION FILES
5.1. Wavelength Calibration
5.1.1. Derivation of the FUSE Wavelength Calibration
Our principal wavelength-calibration target is
GCRV 12336, central star of the planetary nebula M 27,
whose spectrum exhibits a myriad of molecular-hydrogen
absorption features (McCandliss et al. 2007). For the
SiC1B channel, these data are supplemented at the
shortest wavelengths by spectra of the hot white dwarf
G 191-B2B (Lemoine et al. 2002). Spectra obtained
through each of the three FUSE apertures were fully
reduced, corrected for astigmatism, and used to derive
an empirical mapping of pixel to wavelength. For each
channel, standard optical expressions were used to
derive a theoretical dispersion solution, which was fit
to the empirical data with only its constant term (the
zero-point of the wavelength scale) as a free parameter.
The shifted theoretical dispersion solution was used to
generate the wavelength-calibration file.
Early versions of the pipeline relied on the wavelength
calibration to correct for non-linearities in the detector X
scale (the geometric distortion discussed in § 4.3.6). Cor-
recting for this effect separately has greatly improved the
accuracy of the FUSE wavelength scale. To determine
the geometric distortion in the X dimension, a spline
was fit to the residuals from each aperture (expected mi-
nus observed X coordinate of each absorption feature;
Fig. 8). In practice, residuals from all six apertures (both
LiF and SiC channels) were included in the fit, but data
from the other five apertures were weighted 100 times
less than those for the aperture being fitted. The addi-
tional data points help to constrain the fit in wavelength
regions where the data are sparse or missing. The spline
fits from all six apertures were then used to construct the
two-dimensional map of detector distortions in the X di-
mension that is used by the geometric-distortion routine
described in § 4.3.6. The process is iterative, with the
residuals (ideally) becoming smaller with each iteration.
The scatter of individual measurements about the
spline fit is caused in some cases by blended absorption
lines and in others by localized distortions induced by
the fiber-bundle structure of the MCPs. This scatter is
thus a fair estimate of the inaccuracies that the user may
expect in the relative measurement of the wavelength of
any given feature. The wavelength inaccuracies caused
by localized distortions are 3–4 detector pixels (0.025 Å
or 7 km s−1) at most wavelengths, but may be as large
as 6–8 pixels. They occur in tiny windows about 1 to 3 Å
wide, depending on the channel and segment. Some data
sets show larger residuals. These distortions are inherent
in the FUSE data set and represent the ultimate limit
to the accuracy of the FUSE wavelength calibration.
5.1.2. Zero-Point Uncertainties
The FUSE wavelength calibration assumes that the
motion-corrected spectrum falls at a precise location on
the detector. If it is shifted in X, then the wavelength
scale of the extracted spectrum will suffer a zero-point
offset. For the guide channel (either LiF1 or LiF2),
the dominant source of wavelength errors is thermally-
induced rotations of the spectrograph gratings, which de-
pend on the satellite attitude. For the other channels,
additional wavelength errors come from mirror misalign-
ments that shift the target away from the center of the
aperture. Such misalignments may produce zero-point
offsets of up to ±0.15 Å for point sources in the LWRS
aperture. Offsets are less than ±0.02 Å for the MDRS
aperture and are negligible for the HIRS aperture.
We define the zero point of our wavelength scale by
requiring that the Lyman β airglow feature, observed
through the HIRS aperture and processed as if it were a
point source, be at rest in spacecraft coordinates when all
Doppler corrections are turned off. The use of an airglow
feature eliminates errors due to mirror motions in the
non-guide channels, but not errors in the grating-motion
correction, so we measure the Lyman β line in some 200
background exposures and shift their mean velocity to
0 km s−1. For each channel, all three apertures (HIRS,
MDRS, and LWRS) use this HIRS-derived wavelength
scale (WAVE CAL version 022 and greater).
The grating-motion correction (§ 4.5.4) is designed to
place the centroid of each Lyman β airglow feature at
a fixed location in FARF coordinates. On average, it
achieves that goal: for our sample of 200 background ex-
posures, the measured velocity of the Lyman β line has a
standard deviation of between 2 and 3 km s−1, depend-
ing on the channel. Unfortunately, some combinations
of pole and beta angle are not well corrected, leading
to velocity offsets of 10 km s−1 or more, and additional
motions – of either the gratings or some other optical
component – can shift the extracted spectra by several
km s−1 from one exposure to the next.
Figure 9 presents the measured wavelength of the in-
terstellar O I λ1039 absorption feature in 47 exposures of
the hot white dwarf KPD 0005+5106 obtained through
the HIRS aperture. The 2001 and 2002 data show little
scatter and yield a mean velocity of −10.7± 1.9 km s−1.
(Holberg, Barstow, and Sion 1998 report a heliocentric
velocity of −7.50 ± 0.76 km s−1 for the interstellar fea-
tures along this line of sight.) The 2003 data (all from
a single observation) span nearly 10 km s−1. The 2004
data (again from a single observation) are tightly corre-
lated but offset by∼ 7 km s−1. These data were obtained
at a spacecraft orientation (beta and pole angles) that is
generally well corrected by our grating-motion algorithm;
apparently, some other effect is at work. The 2006 data
come from the LiF2 channel, which became the default
guide channel in 2005 (§ 6.1).
We do not recommend the general use of airglow lines
to fix the absolute wavelength scale of point-source spec-
tra for several reasons: First, airglow emission fills the
aperture, so the resulting airglow lines provide no in-
formation about the position of the target relative to
the aperture center. Second, the jitter correction (for all
channels) and the mirror-motion correction (for the SiC
channels) are inappropriate for airglow emission. Third,
the Doppler correction for the spacecraft’s orbital motion
can degrade their resolution.
CalFUSE: The FUSE Calibration Pipeline 17
TABLE 1
Stellar Parameters Adopted for FUSE
Flux Standards
Teff log g Vrad
Name (K) (cm s−2) (km s−1)
GD 71 32,843 7.783 80.0
GD 659 35,326 7.923 33.0
GD 153 39,158 7.770 50.0
HZ 43 50,515 7.964 20.6
GD 246 53,000 7.865 −13.2
G 191-B2B 61,200 7.5 · · ·
Note. — For G 191-B2B, we use the model
employed by Kruk et al. 1999 for the final
Astro-2 calibration of HUT.
5.1.3. Diffuse Emission
The FUSE wavelength scale is derived from
astigmatism-corrected, point-source spectra. Extended-
source (diffuse) spectra are not corrected for astig-
matism. If point-source data are processed with the
astigmatism correction turned off, the resulting wave-
length errors are less than about 4 detector pixels,
consistent with the uncertainties in the wavelength
scale. Therefore, the present FUSE wavelength calibra-
tion should be adequate for extended-source spectra.
Airglow lines are useful for determining the zero-point
for extended sources that fill the aperture.
5.2. Flux Calibration
5.2.1. Derivation of the Effective-Area Curve
The FUSE flux calibration is based on in-flight ob-
servations of the well-studied DA (pure-hydrogen) white
dwarfs listed in Table 1, which have been observed at reg-
ular intervals throughout the mission. For each channel,
data from multiple stars are combined to track changes
in the instrument sensitivity using a technique similar
to that developed by Massa and Fitzpatrick (2000) for
the International Ultraviolet Explorer (IUE)/ satellite.
The algorithm yields a series of time- and wavelength-
dependent sensitivity curves as well as the spectrum of
each star, in units of raw counts, as it would have ap-
peared on a date early in the mission, which we choose
to be T0 = 1999 December 31. (We refer to the latter as
“T0 spectra.”)
For each star, we generated a synthetic spectrum using
the programs TLUSTY (version 200) and SYNSPSEC
(version 48) of Hubeny and Lanz (1995). The non-LTE
pure-hydrogen model atmospheres were computed ac-
cording to a prescription by Hubeny (private commu-
nication) using 200 atmospheric layers to ensure an opti-
mal absolute flux accuracy. The atmospheric parameters
listed in Table 1, consistent with HST, IUE, and opti-
cal observations, were used to compute the models (Hol-
berg, private communication). For G 191-B2B, we used
the model employed by Kruk et al. (1999) for the final
Astro-2 calibration of HUT. Observations of these stars
with the Faint Object Spectrograph aboard HST have
shown that the models, including parameter uncertain-
ties, are consistent to within 2% at wavelengths longer
than Lyman α (Bohlin, Colina, and Finley 1995; Bohlin
1996). Uncertainties in the far-ultraviolet waveband are
slightly higher, as discussed by Kruk et al. (1999).
For each channel, the effective area in units of cm2 is
computed by dividing one or more T0 spectra in units of
counts s−1 Å−1 by a synthetic white-dwarf spectrum in
units of photons cm−2 s−1 Å−1. We find excellent agree-
ment between the effective areas derived from the differ-
ent standard stars. Sensitivity curves for the LiF1A and
SiC1A channels are presented in Fig. 10. (Effective-area
curves for all FUSE channels are available from MAST.)
The sensitivity of the LiF1A channel decreased by ∼ 15%
over the first three years of the mission, but appears to
have stabilized; that of the SiC1 channel has declined by
∼ 45% since launch and is falling still (though slowly).
Effective-area curves (AEFF CAL) for each channel and
detector segment were generated at three-month inter-
vals until the loss of the third reaction wheel in 2004
December; we plan to generate them at six-month inter-
vals for the duration of the mission.
Caveats: We do not attempt to correct spectra ob-
tained through the MDRS and HIRS apertures for
changes in instrument sensitivity, but employ a single
effective-area curve for each. The low throughput of
these apertures, combined with the likelihood that their
spectra are non-photometric, makes tracking changes in
their sensitivity both more difficult and less useful than
for the LWRS aperture.
5.2.2. Systematic Uncertainties
The greatest uncertainties in a line or continuum flux
derived from a FUSE spectrum are due to systematic
effects. An estimate of the uncertainty in our flux cali-
bration can be obtained by comparing the effective-area
curves derived from different white-dwarf stars. Differ-
ences among the curves reflect errors in both the model
atmospheres and the stellar parameters upon which they
are based. In most channels, the scatter in the derived
effective areas is between 2 and 4%.
The photometric accuracy of FUSE spectra is subject
to numerous effects that cannot be fully corrected by the
CalFUSE pipeline. A target centered in an aperture of
the guide channel (LiF1 or LiF2) may not be centered
in the corresponding apertures of the other three chan-
nels. Since the loss of the first two reaction wheels in
2001, spacecraft drifts may move the target out of even
the guide-channel aperture. While the pipeline does at-
tempt to flag times when the target is out of the aper-
ture, the algorithm used is conservative in that it un-
derestimates the time lost to pointing errors (§ 4.4.6).
The user is advised to consult the count-rate plots gen-
erated by the pipeline (suffix “rat.gif”; § 4.10) and the
LIF CNT RATE and SIC CNT RATE arrays of the IDF
timeline table to determine the photometric quality of an
exposure. Using tools available from MAST or the user-
defined good-time intervals discussed in § 4.4.7, users can
reject time periods when the count rate is low or re-scale
the flux of low-count-rate exposures.
When a point-source target falls near the top or bottom
edge of an aperture, vignetting in the spectrograph may
attenuate the target flux in a wavelength-dependent way.
Astigmatism gives FUSE spectra the shape of a bow tie
(Fig. 3). If vignetting is important, then the spectrum
will lie below the center of the aperture on one side of
the bow tie and above it on the other. Significant flux
loss is possible in wavelength regions far from the center
of the bow tie.
18 Dixon et al.
Other systematic uncertainties are imposed by various
detector flat-field effects; their relative importance de-
pends upon one’s scientific goals. For narrow emission
lines, flux uncertainties are dominated by the moiré pat-
tern (high-frequency ripples due to beating among the
arrays of microchannel pores in the MCP stack; § 6.4),
unless the observation was obtained using an FP split or
the equivalent was achieved via grating and mirror mo-
tions. For broad features, the moiré is not important,
but larger-scale flat-field features are. These effects are
discussed in The FUSE Instrument and Data Handbook.
Finally, when fitting a spectral energy distribution, the
greatest uncertainty is caused by worms (§ 6.3), which
may depress the observed flux over tens of Ångstroms by
50% or more.
5.2.3. Extended Sources
The FUSE flux calibration is derived from point-
source targets. Because the distribution of flux in the
cross-dispersion direction differs for point and extended
sources, it is possible that the instrumental sensitivity
may also differ; this question has not been explored in
detail. Extended spectra are less affected by worms (§
6.3) than are point-source spectra. Moreover, because
the spectrum of a diffuse emitter is spread over a larger
region of the detector, it will suffer less from local flat-
field effects.
6. DISCUSSION
6.1. Spacecraft Guiding on the LiF2 Channel
The switch from FES A to FES B as the default guide
camera in 2005 July has two principal effects on the qual-
ity of FUSE data. First, tracking with FES A ensured
that targets remained in the center of the LiF1 aperture,
which is the most sensitive channel in the astrophysically-
important 1000–1100 Å waveband. Tracking with FES
B will keep targets centered in the LiF2 aperture, in-
creasing the likelihood of data loss in the LiF1 channel.
Second, in order to optimize the optical focus of FES B,
the LiF2 FPA was moved out of the focal plane of the
LiF2 primary mirror. Observations of point sources with
the LWRS aperture are unaffected, and the point-source
spectral resolution of this channel is unchanged, but the
throughput of the narrow LiF2 apertures is reduced. The
effective transmission of the apertures has not been char-
acterized in detail, but is approximately 70% for LIF2
MDRS and 15% for LiF2 HIRS, versus 98% and 60% for
their LiF1 counterparts. The spectral resolution for dif-
fuse sources is expected to be slightly lower in LiF2 than
in LiF1.
6.2. Scattered Solar Emission
In addition to airglow lines, scattered solar emission
features are present in the SiC channels when observ-
ing at high beta angles during the sunlit portion of the
orbit. Emission from C III λ977.0, Lyman β λ1025.7,
and O VI λλ1031.9, 1037.6 has been positively identified.
Emission from N III λ991.6 and N II λ1085.7 may also
be present. It is believed that sunlight is scattered by
reflective, silver-coated Teflon blankets lying above the
SiC baffles. At low beta angles, scattered solar emission
is less apparent, because the blankets are shaded by the
SiC baffles and the open baffle doors and because the ra-
diation strikes the blankets at a high angle of incidence.
It is unknown at which beta angle, if any, the solar emis-
sion completely disappears. Because the LiF channels
lie on the shadowed side of the spacecraft, solar emission
lines are not seen in LiF spectra. C III and O VI emission
observed in the SiC channels during orbital day should
always be compared with the emission observed either
with the LiF channel or during the nighttime portion of
an orbit.
Since the failure of the third reaction wheel in 2004
December, FUSE mission controllers have experimented
with the use of non-standard roll angles to improve space-
craft stability. These roll angles can place the spacecraft
in a configuration that greatly increases the sunlight scat-
tered into one of the SiC channels. The scattered light,
mostly Lyman continuum emission, appears as an in-
crease in the background at wavelengths shorter than
about 920 Å; strong, resolved Lyman lines are present at
longer wavelengths. When present, it is generally seen
in only one of the two SiC channels. We have no way to
model or subtract this emission.
6.3. The Worm
The spectra of point-source targets occasionally exhibit
a depression in flux that may span as much as 50 Å
(Fig. 11). These depressions appear in detector images
as narrow stripes roughly parallel to the dispersion axis
(Fig. 12). The stripes, known as worms, can attenuate
as much as 50% of the incident light in affected portions
of the spectrum. Worms shift in the dispersion direction
when the target moves in the aperture. They are due
to an unfortunate interaction between the horizontal fo-
cus of the spectrograph and the innermost wire grid (the
quantum-efficiency grid; § 4.3.6). Since the location of
this focus point is a function of wavelength, the strength
of a worm is exquisitely sensitive to the exact position of
the spectrum on the detector. We cannot determine this
position with sufficient precision to correct reliably for
flux lost to worms. Though most prominent in LiF1B
LWRS spectra, worms can appear in all channels and
apertures. Observers who require absolute spectropho-
tometry should carefully examine FUSE spectral image
files for the presence of worms. The redundant wave-
length coverage of the various FUSE channels can be
used to mitigate their effects.
6.4. The Moiré Pattern in Histogram Data
Since the release of CalFUSE v3.0, users have reported
strong, non-Gaussian noise in the spectra of some bright
stars observed in histogram mode. An example is shown
in Fig. 13. The high-frequency ripples have a period of
approximately 9 detector pixels, or about 0.06 Å. These
ripples are a moiré pattern due to beating among the
arrays of microchannel pores in the three layers of the
MCP stack (Tremsin et al. 1999). The moiré fringes are
strongest on segment 2B, but are also visible on segments
1A and 2B. The motion corrections applied to time-tag
data tend to smooth out this effect, but it can be quite
strong in histogram data. Where it is present, users are
advised to smooth or bin their spectra by at least one
resolution element to reduce its effects. This and other
detector artifacts are described in the FUSE Instrument
CalFUSE: The FUSE Calibration Pipeline 19
and Data Handbook.
6.5. A Note about Time
The FUSE spacecraft uses Coordinated Universal
Time (UTC). The spacecraft clock is updated periodi-
cally from the ground using a procedure that corrects
for the signal transit time from the ground station to the
spacecraft. The ground station time comes from GPS
satellites. The Instrument Data System receives a 1 Hz
signal from the spacecraft that is used to align the IDS
clock with the spacecraft clock to an accuracy of ±5 ms.
In time-tag mode, the IDS typically inserts a time stamp
into the data stream once per second, but can insert time
stamps as frequently as 125 times per second. Unfortu-
nately, the binary format of the time stamp rounds the
time value to the nearest 1/128 of a second. The two pe-
riods beat against one another, causing the loss of three
time stamps each second. Additional timing uncertain-
ties due to delays in the detector electronics have not
been measured, but are assumed to be on the order of
a few milliseconds. For most time-tag observations, for
which time stamps are recorded only once per second,
these effects can safely be ignored.
Raw time-tag files are constructed by assigning the
value of the most recent time stamp, in units of seconds
from the exposure start time, to each subsequent photon
event. The frequency of these time markers determines
the temporal resolution of the data. Photon-arrival times
are not modified by the pipeline: values are UTC as as-
signed by the IDS. In particular, photon-arrival times are
not converted to a heliocentric scale.
6.6. Combining Data from Multiple Exposures
For each FUSE observation, OPUS combines data
from individual exposures into a set of observation-level
spectra, as described in § 4.11. While these files are suffi-
cient for many projects, other projects may benefit from
specialized data processing. Here are some points to keep
in mind when combining FUSE data from multiple expo-
sures: For bright targets, the goal is to maximize spectral
resolution, so it is important to align precisely the spectra
from individual exposures before combining them. The
wavelength zero points of segments A and B are consis-
tent across each of the FUSE detectors (§ 5.1), so shifts
measured for one detector segment can safely be applied
to the other. For observations made before 2005 July, the
LiF1 spectrum is likely to have the most accurate wave-
length scale, so it serves as the standard for the other
three channels. For later observations, the LiF2 spectra
are likely to be the most accurate. A procedure to cross-
correlate and shift spectra by hand is described in The
FUSE Data Analysis Cookbook. When cross-correlating
the spectra of point-source targets, it is important to ex-
clude regions contaminated by airglow features, as their
motions are unlikely to track those of the target. For
faint targets, the goal is to optimize the fidelity of the
background model by maximizing the signal-to-noise ra-
tio on background regions of the detector, a goal achieved
by combining the IDFs from multiple exposures before
extracting the spectra. A variety of C- and IDL17-based
tools to perform these and other data-analysis tasks has
17 IDL is a registered trademark of ITT Corporation for their
Interactive Data Language software.
TABLE 2
Format of Raw Time-Tag Files
Array Name Format Description
Primary Header-Data Unit (HDU 1)
Header only. Keywords contain exposure-specific information.
HDU 2: Photon Event List
TIME FLOAT Photon arrival time (seconds)
X SHORT Raw X position (0–16383)
Y SHORT Raw Y position (0–1023)
PHA BYTE Pulse height (0–31)
HDU 3: Good-Time Intervals
START DOUBLE GTI start time (seconds)
STOP DOUBLE GTI stop time (seconds)
Note. — Times are relative to the exposure start time, stored
in the header keyword EXPSTART.
been generated by the FUSE project. Software and doc-
umentation are available from MAST.
We acknowledge with gratitude the efforts of those who
contributed to the design and implementation of initial
versions of the CalFUSE pipeline and its associated cal-
ibration files: G. A. Kriss, E. M. Murphy, J. Murthy,
W. R. Oegerle, and K. C. Roth. This research has made
use of the Multimission Archive at the Space Telescope
Science Institute (MAST). STScI is operated by the As-
sociation of Universities for Research in Astronomy, Inc.,
under NASA contract NAS5-26555. Support for MAST
for non-HST data is provided by the NASA Office of
Space Science via grant NAG5-7584 and by other grants
and contracts. This work is supported by NASA contract
NAS5-32985.
Facility: FUSE
APPENDIX
A. FILE FORMATS
All FUSE data are stored as FITS files (Hanisch et al.
2001) containing one or more Header + Data Units
(HDUs). The first is called the primary HDU (or
HDU 1); it consists of a header and an optional N-
dimensional image array. The primary HDU may be fol-
lowed by any number of additional HDUs, called “exten-
sions.” Each extension has its own header and data unit.
FUSE employs two types of extensions, image extensions
(a 2-dimensional array of pixels) and binary table exten-
sions (rows and columns of data in binary representa-
tion). CalFUSE uses the CFITSIO subroutine library
(Pence 1999) to read and write FITS files.
A-1. Raw Time-Tag and Histogram Files
FUSE raw data files are generated by OPUS using
both data downlinked by the telescope and information
from the FUSE Mission Planning Database (§ 4.1). In-
formation regarding the target, exposure times, instru-
ment configuration, and engineering parameters is stored
in a series of header keywords in the primary HDU. All
header keywords are described in the FUSE Instrument
and Data Handbook. In raw time-tag files (Table 2), the
20 Dixon et al.
primary HDU consists of a header only, with no asso-
ciated image array. HDU 2 contains the photon-event
list, with arrival time (in seconds from the exposure start
time), raw detector coordinates, and pulse height for each
event in turn. HDU 3 lists good-time intervals (GTIs)
calculated by OPUS. Raw time-tag file names end with
the suffix “ttagfraw.fit.” They can be as large as 10–20
MB for the brightest targets.
The data in raw histogram files (suffix “histfraw.fit”)
are stored as a series of image extensions (Table 3).
The primary HDU contains the same header keywords
as time-tag files, along with a small (8 × 64 pixel) im-
age called the Spectral Image Allocation (SIA) table.
The SIA table is used to map regions of the detector
to on-board memory. Each element in the SIA table
corresponds to a 2048 × 16 pixel region on a detector
segment. If the element is set to 1, the photons from
the corresponding region are saved; if 0, they are dis-
carded. Additional image extensions follow, each con-
taining the binned image of some region of the detector;
these regions may overlap. In general, science data are
binned by 8 pixels in Y and unbinned in X; binning fac-
tors for each exposure are stored in the header keywords
SPECBINX and SPECBINY. While the format given in
Table 3 is standard, any number of image extensions may
be present in a histogram file. Raw histogram data files
are 1–1.5 MB in size.
A-2. Housekeeping and Jitter Files
For each exposure, a single housekeeping file is gen-
erated by OPUS from engineering data supplied by the
spacecraft (§ 4.1). Housekeeping files (suffix “hskpf.fit”)
contain 62 arrays, including spacecraft pointing informa-
tion, detector voltage levels, and various counter values,
in a single binary table extension. Arrays are tabulated
once per second, though most parameters are updated
only once every 16 seconds. Only a few of the house-
keeping arrays are employed by the pipeline. The detec-
tor high voltage and LiF, SiC, FEC, and AIC counter
arrays are used to populate the corresponding arrays in
the IDF timeline table (§ A-3).
From pointing information in the housekeeping file,
OPUS derives a jitter file (suffix “jitrf.fit”) consisting of
a single binary table extension with 4 columns: TIME,
DX, DY and TRKFLG. The time refers to the elapsed
time (in seconds) from the start of the exposure. Since
the engineering data commonly begin up to a minute be-
fore the exposure, the first few entries of this array are
negative. DX and DY are the offsets along the X (disper-
sion) and Y (cross-dispersion) directions in arc seconds.
These offsets are defined relative to the commanded posi-
tion of the telescope (presumably the target coordinates).
Finally, TRKFLG is the tracking quality flag. Its value
is −1 if the spacecraft is not tracking properly and 0 if
tracking information is unavailable. Values between 1
and 5 represent increasing levels of fidelity for DX and
Additional details regarding the contents and format
of the housekeeping and jitter files are provided in The
FUSE Instrument and Data Handbook.
A-3. Intermediate Data File (IDF)
The IDF (suffix “idf.fit”) contains three FITS binary
table extensions; their contents are listed in Table 4. The
file’s primary header-data unit (HDU 1) is copied directly
from the raw data file. (For histogram data, the SIA ta-
ble is discarded.) Various keywords are populated by the
initialization routine (§ 4.2) and by subsequent pipeline
modules. The first binary-table extension (HDU 2) con-
tains the photon events themselves. For time-tag data,
the TIME, XRAW, YRAW, and PHA arrays are copied
from the raw data file, and the WEIGHT array is ini-
tialized to 1.0. For histogram data, each image pixel
is mapped back to its coordinates on the full detector,
which are recorded in the XRAW and YRAW arrays.
The WEIGHT array is initialized to the number of pho-
ton events in the pixel. Zero-valued pixels are ignored.
Histogram data are not “unbinned.” Each entry of the
TIME array is set to the midpoint of the exposure and
each entry of the PHA array to 20. (Both arrays are
subsequently modified.)
The first pipeline module (§ 4.3) corrects for various
detector effects; it scales the WEIGHT array to correct
for detector dead time and populates the XFARF and
YFARF arrays. (The flight alignment reference frame
represents the output of an ideal detector.) Each pho-
ton event is assigned to one of six aperture-channel com-
binations or to the background (Table 5) and a corre-
sponding code is written to the CHANNEL array (§ 4.5).
After corrections for mirror, grating, and spacecraft mo-
tions, the photon’s final coordinates are recorded in the
X and Y arrays. Though floating-point arrays, XFARF,
YFARF, X, and Y are written to the IDF as arrays of
8-bit integers using the FITS TZERO and TSCALE key-
words. This process effectively rounds each element of
XFARF and X to the nearest 0.25 of a detector pixel
and each element of YFARF and Y to the nearest 0.1 of
a detector pixel.
The screening routines (§ 4.4) use information from the
timeline table (described below) to identify photons that
violate pulse-height limits, limb-angle constraints, etc.
“Bad” photons are not deleted from the IDF, but merely
flagged. Flags are stored as single bits in an 8-bit byte.
We use two sets of flags, TIMEFLGS for time-dependent
and LOC FLGS for location-dependent effects (Table 6).
For each bit, a value of 0 indicates that the photon is
“good,” except for the day/night flag, for which 0 = night
and 1 = day. It is possible to modify these flags without
re-running the pipeline. For example, one could exclude
day-time photons or include data taken close to the earth
limb.
The LAMBDA array contains the heliocentric wave-
length assigned to each photon (§ 4.6), and the ERGCM2
array records its “energy density” in units of erg cm−2 (§
4.7). To convert an extracted spectrum to units of flux,
one must divide by the exposure time and the width of
an output spectral bin.
The second extension (HDU 3) is a list of good-time
intervals (GTIs). The initial values are copied from the
raw data file, but they are modified by the pipeline once
the various screening routines have been run. By con-
vention, the START value of each GTI corresponds to
the arrival time of the first photon in that interval. The
STOP value is one second later than the arrival time of
the last photon in that interval. The length of the GTI
is thus STOP−START.
The third extension (HDU 4) is called the timeline ta-
CalFUSE: The FUSE Calibration Pipeline 21
ble. It contains status flags and spacecraft and detector
parameters used by the pipeline. An entry in the time-
line table is created for each second of the exposure. For
time-tag data, the first entry corresponds to the time of
the first photon event, and the final entry to the time of
the final photon event plus one second. (Should an expo-
sure’s photon-arrival times purport to exceed 55 ks, we
create timeline entries only for each second in the good-
time intervals.) For histogram data, the first element of
the TIME array is set to zero and the final element to
EXPTIME+1 (where EXPTIME is the exposure dura-
tion computed by OPUS). Because we require that EXP-
TIME equal both Σ (STOP−START), summed over all
entries in the GTI table, and the number of good times
in the timeline table, we must flag the final second of
each GTI as bad. No photons are associated with the
STOP time of a GTI.
Only the day/night and OPUS flags of the STA-
TUS FLAGS array are populated when the IDF is cre-
ated; the other flags are set by the various screening
routines (§ 4.4). The elements of the TIME SUNSET,
TIME SUNRISE, LIMB ANGLE, LONGITUDE, LAT-
ITUDE, and ORBITAL VEL arrays are computed from
the orbital elements in the FUSE.TLE file. The
HIGH VOLTAGE array is populated with values from
the housekeeping file. The LIF CNT RATE and
SIC CNT RATE arrays are initially populated with val-
ues derived from the LiF and SiC counter arrays in the
housekeeping file. For time-tag data, these arrays are
eventually updated with the actual count rates within
the target aperture, excluding regions contaminated by
airglow. The FEC CNT RATE and AIC CNT RATE,
described in § 4.3.2, are also derived from counter ar-
rays in the housekeeping file. For time-tag data, the
BKGD CNT RATE array is populated by the burst-
rejection routine (§ 4.4.5) and represents the count rate
in pre-defined background regions of the detector, ex-
cluding airglow features. The array is not populated for
histogram data. The YCENT LIF and YCENT SIC ar-
rays trace the centroid of the target spectra with time
before motion corrections are applied. These two arrays
are not used by the pipeline.
Raw time-tag files (§ A-1) employ the standard FITS
binary table format, listing TIME, X, Y, PHA for each
photon event in turn. The intermediate data files have a
slightly different format, listing all of the photon arrival
times, then the X coordinates, then the Y coordinates.
Formally, the table has only one row, and each element of
the table is an array. (To use the STSDAS terminology,
IDFs are written as 3-D tables.) The MDRFITS func-
tion from the IDL Astronomy User’s Library (Landsman
1993) can read both file formats; some older FITS read-
ers cannot. Note that, because HDUs 2 and 4 of the
IDFs contain floating-point arrays stored as shorts (using
the TZERO and TSCALE keywords), calls to MRDFITS
must include the keyword parameter FSCALE.
A-4. Bad-Pixel Maps (BPM Files)
The BPM files (suffix “bpm.fit”; § 4.8) consist of a
single binary table extension. Its format is similar to
that of the IDF, but it contains only five columns: X, Y,
CHANNEL, WEIGHT, and LAMBDA. The WEIGHT
column, whose values range from 0 to 1, represents the
fraction of the exposure that each pixel was affected by
a dead spot. The BPM files are not archived, but can
be generated from the IDF and jitter file using pipeline
software available from MAST.
A-5. Extracted Spectral Files
Extracted spectra (suffix “fcal.fit”; § 4.9) are stored
in a single binary table extension. Its contents are pre-
sented in Table 7. Note that the spectra are binned in
wavelength. The bin size can be set by the user, but the
default is 0.013 Å, which corresponds to about 2 detec-
tor pixels or about one-fourth of a spectral resolution el-
ement. The WAVE array records the central wavelength
of each spectral bin. For time-tag data, the COUNTS
array represents the total of all (raw) photon events as-
signed to the target aperture. For histogram data, the
COUNTS array is simply the WEIGHTS array divided
by the mean dead-time correction for the exposure. If op-
timal extraction is performed, the values of the FLUX,
ERROR, WEIGHTS, and BKGD arrays are determined
by that algorithm. As a result, the ratio of WEIGHTS
to COUNTS is constant only for histogram data. The
QUALITY array records the percentage of the extrac-
tion window containing valid data. It is 100 if no bad
pixels fell within the wavelength bin, 0 if the entire bin
was lost to bad pixels.
REFERENCES
Bohlin, R. C. 1996, AJ, 111, 1743
Bohlin, R. C., Colina, L., and Finley, D. S. 1995, AJ, 110, 1316
Feldman, P. D., Sahnow, D. J., Kruk, J. W., Murphy, E. M., and
Moos, H. W. 2001, J. Geophys. Res., 106, 8119
Hanisch, R. J., Farris, A., Greisen, E. W., Pence, W. D.,
Schlesinger, B. M., Teuben, P. J., Thompson, R. W., and
Warnock, A. 2001, A&A, 376, 359
Holberg, J. B., Barstow, M. A., and Sion, E. M. 1998, ApJS, 119,
Horne, K. 1986, PASP, 98, 609
Hubeny, I. and Lanz, T. 1995, ApJ, 439, 875
Kruk, J. W., Brown, T. M., Davidsen, A. F., Espey, B. R.,
Finley, D. S., and Kriss, G. A. 1999, ApJS, 122, 299
Landsman, W. B. 1993, in ASP Conf. Ser. 52: Astronomical Data
Analysis Software and Systems II, ed. R. J. Hanisch, R. J. V.
Brissenden, and J. Barnes (San Francisco: ASP), p. 246.
Lemoine, M. et al. 2002, ApJS, 140, 67
Massa, D. and Fitzpatrick, E. L. 2000, ApJS, 126, 517
McCandliss, S. R., France, K., Lupu, R. E., Burgh, E. B.,
Sembach, K., Kruk, J., Andersson, B-G, and Feldman, P. D.
2007, ApJ, in press (astro-ph/0701439)
Moos, H. W. et al. 2000, ApJ, 538, L1
Pence, W. 1999, in ASP Conf. Ser. 172: Astronomical Data
Analysis Software and Systems VIII, ed. D. M. Mehringer,
R. L. Plante, and D. A. Roberts (San Francisco: ASP), p. 487
Rose, J. F., Heller-Boyer, C., Rose, M. A., Swam, M., Miller, W.,
Kriss, G. A., and Oegerle, W. R. 1998, in Proc. SPIE 3349,
Observatory Operations to Optimize Scientific Return, p. 410
Sahnow, D. J. et al. 2000a, ApJ, 538, L7
Sahnow, D. J., Gummin, M. A., Gaines, G. A., Fullerton, A. W.,
Kaiser, M. E., and Siegmund, O. H. 2000b, in Proc. SPIE 4139,
Instrumentation for UV/EUV Astronomy and Solar Missions,
ed. S. Fineschi, C. M. Korendyke, O. H. Siegmund, and B. E.
Woodgate, p. 149
Siegmund, O. H. et al. 1997, in Proc. SPIE 3114, EUV, X-Ray,
and Gamma-Ray Instrumentation for Astronomy VIII, ed.
O. H. Siegmund and M. A. Gummin, p. 283
Tremsin, A. S., Siegmund, O. H., Gummin, M. A., Jelinsky,
P. N., and Stock, J. M. 1999, Appl. Opt., 38, 2240
http://arxiv.org/abs/astro-ph/0701439
22 Dixon et al.
Al+LiF Coated 
Mirror #2
Focal Plane 
Assemblies (4)
Detectors (2) 
Al+LiF Coated 
Mirror #1
SiC Coated 
Mirror #2
SiC Coated 
Mirror #1
Rowland Circles
Al+LiF Coated
Grating #2
Al+LiF Coated
Grating #1
SiC Coated
Grating #2
Fig. 1.— Schematic of the FUSE instrument optical system. The telescope focal lengths are 2245 mm, and the Rowland circle diameters
are 1652 mm. (Figure from Moos et al. 2000.)
CalFUSE: The FUSE Calibration Pipeline 23
-150 -100 -50 0 50 100 150
X Coordinate (arcsec)
Fig. 2.— The FUSE apertures projected onto the sky. In the FPA coordinate system, the LWRS, HIRS, and MDRS apertures are centered
at Y = −118.′′07, −10.′′27, and +90.′′18, respectively. The reference point (RFPT) at X = +55.′′18 is not an aperture; when a target is
placed at this location, the three apertures sample the background sky. With north on top and east on the left, this diagram corresponds
to an aperture position angle of 0◦. Positive aperture position angles correspond to a counter-clockwise rotation of the spacecraft about
the target aperture. This diagram represents only a portion of the FPA; its active area is 19′×19′.
24 Dixon et al.
0 5.0•103 1.0•104 1.5•104
2.0•103 4.0•103 6.0•103 8.0•103 1.0•104 1.2•104 1.4•1040
1000 1020 1040 1060 1080
1000 1020 1040 1060 10801080 1060 1040 1020
1080 1060 1040 1020
Wavelength (Å)
Fig. 3.— Image of detector segment 1A during a bright-earth observation. All lines are geocoronal. Note the strong Lyman β (1026 Å)
feature in each spectrum. The data have been fully corrected for detector and other distortions. Extended-source extraction windows for all
three apertures in both the LiF and SiC channels are marked; point-source extraction windows are somewhat narrower in Y. Instrumental
astigmatism is responsible for the bow-tie shape of each spectrum. The region shown corresponds to detector pixels 900 to 15,300 in X and
0 to 915 in Y.
Fig. 4.— Image of detector 1A in raw X and Y coordinates showing geometric distortion. The image shows only a portion of the detector.
It was constructed from 3 separate exposures with stars in the HIRS, MDRS, and LWRS apertures of the LiF1A detector.
CalFUSE: The FUSE Calibration Pipeline 25
Fig. 5.— Segments of detector 1A images showing filamentary (top) and checkerboard (bottom) bursts. Checkerboard bursts typically
fill the detector, save for the region around the LiF Lyman β lines on detector 1A.
Fig. 6.— Segment of LiF1A spectrum before (bottom) and after (top) astigmatism correction. Note the reduction of curvature in the
absorption features. A detector dead spot is present on the left side of the figure.
Fig. 7.— Night-time scattered-light image for detector 1A. Note the vertical scattered-light stripe to the right of the image center.
26 Dixon et al.
0 5000 10000 15000
X Coordinate (Pixels)
Fig. 8.— Geometric distortion in the X coordinate of the LiF1A LWRS channel. Data represent the difference between the measured
locations of H2 lines in the spectrum of GCRV 12336 and those predicted by a theoretical dispersion relation. The solid line is a spline fit
to the residuals.
0 10 20 30 40 50
Observation Date
2001/09
2002/08
2002/10
2003/12
2004/07
2006/12
Fig. 9.— Measured heliocentric velocity of the interstellar O I λ1039.23 absorption feature in each of 47 exposures of the white dwarf
KPD 0005+5106 through the high-resolution (HIRS) aperture. Holberg et al. 1998 report a heliocentric velocity of −7.50 ± 0.76 km s−1
for the interstellar features along this line of sight.
CalFUSE: The FUSE Calibration Pipeline 27
2000 2002 2004 2006
Date (Year)
LiF1A
SiC1A
980 1000 1020 1040 1060 1080 1100
Wavelength (Å)
LiF1A
SiC1A
Fig. 10.— FUSE sensitivity as a function of time. Upper panel: Effective area of the LiF1A and SiC1A channels, averaged over the
wavelength region 1030–1040 Å. The gap between 2004 October and 2006 May represents the period after the loss of the third reaction
wheel, when few calibration targets were observed. Lower panel: Effective-area curves for the LiF1A and SiC1A channels, dated 1999 and
2006. (For both channels, the 1999 curve has the higher effective area.)
28 Dixon et al.
1100 1120 1140 1160 1180
Wavelength (Å)
Fig. 11.— Point-source spectra showing the effects of the worm. Spectra A and B, obtained with the LiF1B channel, show deep depressions
near 1145 and 1160 Å, respectively. The wavelength of maximum attenuation varies with the Y position of the target within the aperture.
Spectrum C, obtained with the LiF2A channel, is unattenuated.
0 2000 4000 6000
Fig. 12.— Detector images showing the effects of the worm. In these negative images, worms appear as bright stripes parallel to the
dispersion axis. The data shown correspond to spectra A and B in Fig. 11 and span wavelengths between 1134 and 1187 Å.
1044.0 1044.5 1045.0 1045.5 1046.0
Wavelength (Å)
Fig. 13.— Moiré pattern in the LiF2B spectrum of the star HD 209339, obtained in histogram mode. The associated error array is
overplotted. The moiré ripples are strongest on this detector segment, but are also seen on segments 1A and 1B.
CalFUSE: The FUSE Calibration Pipeline 29
TABLE 3
Format of Raw Histogram Files
Image Sizea
HDU Contents (binned pixels)
1b SIA Tablec 8× 64
2 SiC Spectral Image (12–20) × 16384
3 LiF Spectral Image (12–20) × 16384
4 Left Stim Pulse 2 × 2048
5 Right Stim Pulse 2 × 2048
Note. — While this table describes the format of a typical
raw histogram file, any number of HDUs are allowed.
a Quoted image sizes assume the standard histogram binning: by
8 pixels in Y, unbinned in X. Actual binning factors are given in
the primary file header.
b Header keywords of HDU 1 contain exposure-specific informa-
tion.
c The SIA table describes which regions of the detector are in-
cluded in the file.
TABLE 4
Format of Intermediate Data Files
Array Name Format Description
Primary Header-Data Unit (HDU 1)
Header only. Keywords contain exposure-specific information.
HDU 2: Photon Event List
TIME FLOAT Photon arrival time (seconds)
XRAW SHORT Raw X coordinate (0–16383)
YRAW SHORT Raw Y coordinate (0–1023)
PHA BYTE Pulse height (0–31)
WEIGHT FLOAT Photons per binned pixel for HIST data,
initially 1.0 for TTAG data
XFARF FLOAT X coordinate in geometrically-corrected frame
YFARF FLOAT Y coordinate in geometrically-corrected frame
X FLOAT X coordinate after motion corrections
Y FLOAT Y coordinate after motion corrections
CHANNEL BYTE Aperture+channel ID for the photon (Table 5)
TIMEFLGS BYTE Time flags (Table 6)
LOC FLGS BYTE Location flags (Table 6)
LAMBDA FLOAT Wavelength of photon (Å)
ERGCM2 FLOAT Energy density of photon (erg cm−2)
HDU 3: Good-Time Intervals
START DOUBLE GTI start time (seconds)
STOP DOUBLE GTI stop time (seconds)
HDU 4: Timeline Table
TIME FLOAT Seconds from exposure start time
STATUS FLAGS BYTE Status flags
TIME SUNRISE SHORT Seconds since sunrise
TIME SUNSET SHORT Seconds since sunset
LIMB ANGLE FLOAT Limb angle (degrees)
LONGITUDE FLOAT Spacecraft longitude (degrees)
LATITUDE FLOAT Spacecraft latitude (degrees)
ORBITAL VEL FLOAT Component of spacecraft velocity
in direction of target (km/s)
HIGH VOLTAGE SHORT Detector high voltage (unitless)
LIF CNT RATE SHORT LiF count rate (counts/s)
SIC CNT RATE SHORT SiC count rate (counts/s)
FEC CNT RATE FLOAT FEC count rate (counts/s)
AIC CNT RATE FLOAT AIC count rate (counts/s)
BKGD CNT RATE SHORT Background count rate (counts/s)
YCENT LIF FLOAT Y centroid of LiF target spectrum (pixels)
YCENT SIC FLOAT Y centroid of SiC target spectrum (pixels)
Note. — Times are relative to the exposure start time, stored in the header
keyword EXPSTART. To conserve memory, floating-point values are stored as shorts
(using the FITS TZERO and TSCALE keywords) except for TIME, WEIGHT,
LAMBDA and ERGCM2, which remain floats.
30 Dixon et al.
TABLE 5
Aperture Codes for IDF
CHANNEL Array
Aperture LiF SiC
HIRS 1 5
MDRS 2 6
LWRS 3 7
Not in an aperture 0
TABLE 6
Bit Codes for IDF Time and
Location Flags
Bit Value
Time Flags
8 User-defined bad-time interval
7 Jitter (target out of aperture)
6 Not in an OPUS-defined GTI or
Photon arrival time unknown
5 Burst
4 High voltage reduced
3 SAA
2 Limb angle
1 Day/Night flag (N = 0, D = 1)
Location Flags
8 Not used
7 Fill data (histogram mode only)
6 Photon in bad-pixel region
5 Photon pulse height out of range
4 Right stim pulse
3 Left stim pulse
2 Airglow feature
1 Not in detector active area
Note. — Flags are listed in order from
most- to least-significant bit.
TABLE 7
Format of Extracted Spectral Files
Array Name Format Description
Primary Header-Data Unit (HDU 1)
Header only. Keywords contain exposure-specific information.
HDU 2: Extracted Spectrum
WAVE FLOAT Wavelength (Å)
FLUX FLOAT Flux (erg cm−2 s−1 Å−1)
ERROR FLOAT Gaussian error (erg cm−2 s−1 Å−1)
COUNTS INT Raw counts in extraction window
WEIGHTS FLOAT Raw counts corrected for dead time
BKGD FLOAT Estimated background in extraction window (counts)
QUALITY SHORT Percentage of window used for extraction (0–100)

  Since its launch in 1999, the Far Ultraviolet Spectroscopic Explorer (FUSE)
has made over 4600 observations of some 2500 individual targets. The data are
reduced by the Principal Investigator team at the Johns Hopkins University and
archived at the Multimission Archive at Space Telescope (MAST). The
data-reduction software package, called CalFUSE, has evolved considerably over
the lifetime of the mission. The entire FUSE data set has recently been
reprocessed with CalFUSE v3.2, the latest version of this software. This paper
describes CalFUSE v3.2, the instrument calibrations upon which it is based, and
the format of the resulting calibrated data files.

To Appear in Publications of the Astronomical Society of the Pacific
Preprint typeset using LATEX style emulateapj v. 08/22/09
CALFUSE v3: A DATA-REDUCTION PIPELINE FOR THE FAR ULTRAVIOLET SPECTROSCOPIC
EXPLORER1
W. V. Dixon
, D. J. Sahnow
, P. E. Barrett
, T. Civeit
, J. Dupuis
, A. W. Fullerton
, B. Godard
J.-C. Hsu
, M. E. Kaiser
, J. W. Kruk
, S. Lacour
, D. J. Lindler
, D. Massa
, R. D. Robinson
M. L. Romelfanger
, and P. Sonnentrucker
To Appear in Publications of the Astronomical Society of the Pacific
ABSTRACT
Since its launch in 1999, the Far Ultraviolet Spectroscopic Explorer (FUSE) has made over 4600
observations of some 2500 individual targets. The data are reduced by the Principal Investigator
team at the Johns Hopkins University and archived at the Multimission Archive at Space Telescope
(MAST). The data-reduction software package, called CalFUSE, has evolved considerably over the
lifetime of the mission. The entire FUSE data set has recently been reprocessed with CalFUSE v3.2,
the latest version of this software. This paper describes CalFUSE v3.2, the instrument calibrations
upon which it is based, and the format of the resulting calibrated data files.
Subject headings: instrumentation: spectrographs — methods: data analysis — space vehicles: in-
struments — ultraviolet: general — white dwarfs
1. INTRODUCTION
The Far Ultraviolet Spectroscopic Explorer (FUSE) is a
high-resolution, far-ultraviolet spectrometer operating in
the 905–1187 Å wavelength range. FUSE was launched
in 1999 on a Delta II rocket into a nearly circular, low-
earth orbit with an inclination of 25◦ to the equator and
an approximately 100-minute orbital period. Data ob-
tained with the instrument are reduced by the principal
investigator team at the Johns Hopkins University using
a suite of computer programs called CalFUSE. Both raw
and processed data files are deposited in the Multimis-
sion Archive at Space Telescope (MAST).
CalFUSE evolved considerably in the years following
launch as our increasing knowledge of the spectrograph’s
performance allowed us to correct the data for more and
more instrumental effects. The program eventually be-
came unwieldy, and in 2002 we began a project to re-
write the code, incorporating our new understanding of
the instrument and best practices for data reduction.
1 Based on observations made with the NASA-CNES-CSA Far
Ultraviolet Spectroscopic Explorer. FUSE is operated for NASA by
the Johns Hopkins University under NASA contract NAS5-32985.
2 Department of Physics and Astronomy, Johns Hopkins Univer-
sity, 3400 N. Charles Street, Baltimore, MD 21218
3 Space Telescope Science Institute, ESS/SSG, 3700 San Martin
Drive, Baltimore, MD 21218
4 Current address: Earth Orientation Department, U.S. Naval
Observatory, 3450 Massachusetts Avenue NW, Washington, DC
20392
5 Primary affiliation: Centre National d’Études Spatiales, 2 place
Maurice Quentin, 75039 Paris Cedex 1, France
6 Current address: Canadian Space Agency, 6767 route de
l’Aéroport, Longueuil, QC, Canada, J3Y 8Y9
7 Primary affiliation: Department of Physics and Astronomy,
University of Victoria, P. O. Box 3055, Victoria, BC V8W 3P6,
Canada
8 Current address: Institut d’Astrophysique de Paris, 98 bis,
boulevard Arago, 75014 Paris, France
9 Retired
10 Current address: Sydney University, NSW 2006, Australia
11 Sigma Space Corporation, 4801 Forbes Boulevard, Lanham,
MD 20706
12 SGT, Inc., NASA Goddard Space Flight Center, Code 665.0,
Greenbelt, MD 20771
The result is CalFUSE v3, which produces a higher qual-
ity of calibrated data while running ten times faster than
previous versions. The entire FUSE archive has recently
been reprocessed with CalFUSE v3.2; we expect this to
be the final calibration of these data.
In this paper, we describe CalFUSE v3.2.0 and its cal-
ibrated data products. Because this document is meant
to serve as a resource for researchers analyzing archival
FUSE spectra, we emphasize the interpretation of pro-
cessed data files obtained fromMAST rather than the de-
tails of designing or running the pipeline. An overview of
the FUSE instrument is provided in § 2, and an overview
of the pipeline in § 3. Section 4 presents a detailed de-
scription of the pipeline modules and their subroutines.
The FUSE wavelength and flux calibration are discussed
in § 5, and a few additional topics are considered in § 6. A
detailed description of the various file formats employed
by CalFUSE is presented in the Appendix.
Additional documentation available from MAST in-
cludes the CalFUSE Homepage,13 The CalFUSE
Pipeline Reference Guide,14 The FUSE Instrument and
Data Handbook,15 and The FUSE Data Analysis Cook-
book.16
2. THE FUSE INSTRUMENT
FUSE consists of four co-aligned prime-focus tele-
scopes, each with its own Rowland spectrograph (Fig. 1).
Two of the four channels employ Al+LiF optical coatings
and record spectra over the wavelength range∼ 990–1187
Å, while the other two use SiC coatings, which provide
reflectivity to wavelengths below the Lyman limit. The
four channels overlap between 990 and 1070 Å. Spectral
resolution is roughly 20,000 (λ/∆λ) for point sources.
For a complete description of FUSE, see Moos et al.
(2000) and Sahnow et al. (2000a).
At the prime focus of each mirror lies a focal-plane as-
13 http://archive.stsci.edu/fuse/calfuse.html
14 http://archive.stsci.edu/fuse/pipeline.html
15 http://archive.stsci.edu/fuse/dhbook.html
16 http://archive.stsci.edu/fuse/cookbook.html
http://arxiv.org/abs/0704.0899v1
http://archive.stsci.edu/fuse/calfuse.html
http://archive.stsci.edu/fuse/pipeline.html
http://archive.stsci.edu/fuse/dhbook.html
http://archive.stsci.edu/fuse/cookbook.html
2 Dixon et al.
sembly (or FPA, shown in Fig. 2) containing three spec-
trograph entrance apertures: the low-resolution aperture
(LWRS; 30′′ × 30′′), used for most observations, the
medium-resolution aperture (MDRS; 4′′ × 20′′), and the
high-resolution aperture (HIRS; 1.25′′ × 20′′). The ref-
erence point (RFPT) is not an aperture; when a target
is placed at this location, the three apertures sample the
background sky. For a particular exposure, the FITS
file header keywords RA TARG and DEC TARG con-
tain the J2000 coordinates of the aperture (or RFPT)
listed in the APERTURE keyword, while the keyword
APER PA contains the position angle of the −Y axis (in
the FPA coordinate system; see Fig. 2), corresponding to
a counter-clockwise rotation of the spacecraft about the
target (and thus about the center of the target aperture).
The spectra from the four instrument channels are
imaged onto two photon-counting microchannel-plate
(MCP) detectors, labeled 1 and 2, with a LiF spectrum
and a SiC spectrum on each (Fig. 1). Each detector is
comprised of two MCP segments, labeled A and B. Raw
science data from each detector segment are stored in
a separate data file; an exposure thus yields four raw
data files, labeled 1A, 1B, 2A, and 2B. Because the three
apertures are open to the sky at all times, the LiF and
SiC channels each generate three spectra, one from each
aperture. In most cases, the non-target apertures are
empty and sample the background sky. Figure 3 presents
a fully-corrected image of detector 1A obtained during
a bright-earth observation. The emission features in all
three apertures are geocoronal. Note that the LiF1 wave-
length scale increases to the right, while the SiC1 scale
increases to the left. The Lyman β λ1026 airglow feature
is prominent in each aperture.
Two observing modes are available: In photon-address
mode, also known as time-tag or TTAG mode, the X and
Y coordinates and pulse height (§ 4.3.7) of each detected
photon are stored in a photon-event list. A time stamp
is inserted into the data stream, typically once per sec-
ond. Data from the entire active area of the detector are
recorded. Observing bright targets in time-tag mode can
rapidly fill the spacecraft recorder. Consequently, when
a target is expected to generate more than ∼ 2500 counts
s−1 across all four detector segments, the data are stored
in spectral-image mode, also called histogram or HIST
mode. To conserve memory, histogram data are (usu-
ally) binned by eight pixels in Y (the spatial dimension),
but unbinned in X (the dispersion dimension). Only data
obtained through the target aperture are recorded. Indi-
vidual photon arrival time and pulse height information
is lost. The orbital velocity of the FUSE spacecraft is 7.5
km s−1. Since Doppler compensation is not performed
by the detector electronics, histogram exposures must be
kept short to preserve spectral resolution; a typical his-
togram exposure is about 500 s in length.
The front surfaces of the FPAs are reflective in visible
light. On the two LiF channels, light not passing through
the apertures is reflected into a visible-light CCD camera.
Images of stars in the field of view around the apertures
are used for acquisition and guiding by this camera sys-
tem, called the Fine Error Sensor (FES). FUSE carries
two redundant FES cameras, which were provided by
the Canadian Space Agency. FES A views the FPA on
the LiF1 channel, and FES B views the LiF2 FPA. Dur-
ing initial checkout, FES A was designated the default
camera and was used for all science observations until it
began to malfunction in 2005. In July of that year, FES
B was made the default guide camera. Implications of
the switch from FES A to FES B are discussed in § 6.1.
3. OVERVIEW OF CALFUSE
The new CalFUSE pipeline was designed with three
principles mind: the first was that, to the extent possi-
ble, we follow the path of a photon backwards through
the instrument, correcting for the instrumental effects in-
troduced in each step. The principal steps in this path,
together with the effects imparted by each, are listed be-
low. Most of the optical and electronic components in
this list are labeled in Fig. 1.
1. Satellite motion imparts a Doppler shift.
2. Satellite pointing instabilities shift the target image
within (or out of) the aperture.
3. Thermally-induced mirror motions shift the target im-
age within (or out of) the aperture.
4. FPA offsets shift the spectrum on the detector.
5. Thermally-induced motions of the spectrograph grat-
ings shift the target image within (or out of) the aper-
ture.
6. Ion-repelling wire grids can cast shadows called
“worms.”
7. Detector effects include quantum efficiency, flat field,
dead spots, and background.
8. The spectra are distorted by temperature-, count-rate,
time-, and pulse-height-dependent errors in the photons’
measured X and Y coordinates, as well as smaller-scale
geometric distortions in the detector image.
9. Count-rate limitations in the detector electronics and
the IDS data bus are sources of dead time.
To correct for these effects, we begin at the bottom
of the list and (to the extent possible) work backwards.
First, we adjust the photon weights to account for data
lost to dead time (9) and correct the photons’ X and
Y coordinates for a variety of detector distortions (8).
Second, we identify periods of unreliable, contaminated,
or missing data. Third, we correct the photons’ X and
Y coordinates for grating (5), FPA (4), mirror (3), and
spacecraft (2) motions. Fourth, we assign a wavelength
to each photon based on its corrected X and Y coor-
dinates (5), then convert to a heliocentric wavelength
scale (1). Finally, we correct for detector dead spots
(7); model and subtract the detector and scattered-light
backgrounds (7); and extract (using optimal extraction,
if possible), flux calibrate (7) and write to separate FITS
files the target’s LiF and SiC spectra. Note that we can-
not correct for the effects of worms (6) or the detector
flat field (7).
Our second principal was to make the pipeline as mod-
ular as possible. CalFUSE is written in the C program-
ming language and runs on the Solaris, Linux, and Mac
OS X (versions 10.2 and higher) operating systems. The
pipeline consists of a series of modules called by a shell
script. Individual modules may be executed from the
command line. Each performs a set of related correc-
tions (screen data, remove motions, etc.) by calling a
series of subroutines.
Our third principal was to maintain the data as a pho-
ton list (called an intermediate data file, or IDF) un-
til the final module of the pipeline. Input arrays are
read from the IDF at the beginning of each module, and
CalFUSE: The FUSE Calibration Pipeline 3
output arrays are written at the end. Bad photons are
flagged but not discarded, so the user can examine, fil-
ter, and combine processed data files without re-running
the pipeline. Like all FUSE data, IDFs are stored as
FITS files (Hanisch et al. 2001); the various file formats
employed by CalFUSE are described in the Appendix.
A FUSE observation consists of a set of exposures ob-
tained with a particular target in a particular aperture
on a particular date. Each exposure generates four raw
data files, one per detector segment, and each raw data
file yields a pair of calibrated spectra (LiF and SiC), for
a total of 8 calibrated spectral files per exposure. Each
raw data file is processed individually by the pipeline.
Error and status messages are written to a trailer file
(described in § 4.10). Spectra are extracted only for
the target aperture and are binned in wavelength. Bin-
ning can be set by the user, but the default is 0.013 Å,
which corresponds to about two detector pixels or one
fourth of a point-source resolution element. After pro-
cessing, additional software is used to generate a set of
observation-level spectral files, the ALL, ANO, and NVO
files described in § 4.11. A complete list of FUSE data
files and file-naming conventions may be found in The
FUSE Instrument and Data Handbook. All of the expo-
sures that constitute an observation are processed and
archived together.
Investigators who wish to re-process their data may
retrieve the CalFUSE source code and all associated cal-
ibration files from the CalFUSE Homepage. Instructions
for running the pipeline and detailed descriptions of the
calibration files are provided in The CalFUSE Pipeline
Reference Guide. Note that, within the CalFUSE soft-
ware distribution, all of the calibration files, including
the FUSE.TLE file (§ 4.2), are stored in the directory
v3.2/calfiles, while all of the parameter files, including
master calib file.dat and the screening and parameter
files (SCRN CAL and PARM CAL; § 4.2), are stored
in the directory v3.2/parmfiles.
4. STEP BY STEP
In this section, we discuss the pipeline subroutine
by subroutine. Our goal is to describe the algorithms
employed by each subroutine and any shortcomings or
caveats of which the user should be aware.
4.1. OPUS
The Operations Pipeline Unified System (OPUS) is
the data-processing system used by the Space Telescope
Science Institute to reduce science data from the Hub-
ble Space Telescope (HST). We use a FUSE-specific ver-
sion of OPUS to manage our data processing (Rose et al.
1998). OPUS ingests the data downlinked by the space-
craft and produces the data files that serve as input to
the CalFUSE pipeline. OPUS then manages the execu-
tion of the pipeline and the files produced by CalFUSE
and calls the additional routines that combine spectra
from each channel and exposure into a set of observation-
level spectral files. OPUS reads the FUSE Mission Plan-
ning Database (which contains target information from
the individual observing proposals and instrument con-
figuration and scheduling information from the mission
timeline) to populate raw file header keywords and to
verify that all of the data expected from an observation
were obtained.
OPUS generates six data files for each exposure. Four
are raw data files (identified by the suffix “fraw.fit”), one
for each detector segment. One is a housekeeping file
(“hskpf.fit”) containing time-dependent spacecraft engi-
neering data. Included in this file are detector volt-
ages, count rates, and spacecraft-pointing information.
The housekeeping file is used to generate a jitter file
(“jitrf.fit”), which contains information needed to cor-
rect the data for spacecraft motion during an exposure.
Detailed information on the format and contents of each
file is provided in the Appendix.
4.2. Generate the Intermediate Data File
The first task of the pipeline is to convert the raw data
file into an intermediate data file (IDF), which maintains
the data in the form of a photon-event list. (The format
and contents of the IDF are described in § A-3.) For data
obtained in time-tag mode, the module cf ttag init
merely copies the arrival time, X and Y detector coor-
dinates, and pulse-height of each photon event from the
raw file to the TIME, XRAW, YRAW, and PHA arrays of
the IDF. A fifth array, the photon weight, is initially set
to unity. Photons whose X and Y coordinates place them
outside of the active region of the detector are flagged as
described in § 4.3.8. Raw histogram data are stored by
OPUS as an image; the module cf hist init converts
each non-zero pixel of that image into a single entry in
the IDF, with X and Y equal to the pixel coordinates
(mapped to their location on the unbinned detector),
arrival time set to the mid-point of the exposure, and
pulse height set to 20 (possible values range from 0 to
31). The arrival time and pulse height are modified later
in the pipeline. The photon weight is set to the number
of accumulated counts on the pixel, i.e., the number of
photons detected on that region of the detector.
The IDF has two additional extensions. The first con-
tains the good-time intervals (GTIs), a series of start
and stop times (in seconds from the exposure start time
recorded in the file header) computed by OPUS, when
the data are thought to be valid. For time-tag data, this
extension is copied directly from the raw data file. For
histogram data, a single GTI is generated with START =
0 and STOP = EXPTIME (the exposure time computed
by OPUS). The final extension is called the timeline ta-
ble and consists of 16 arrays containing status flags and
spacecraft-position, detector high-voltage, and count-
rate parameters tabulated once per second throughout
the exposure. Only the day/night and OPUS bits of the
time-dependent status flags are populated (§ A-3); the
others are initialized to zero. The spacecraft-position pa-
rameters are computed as described below. The detector
voltages and the values of various counters are read from
the housekeeping data file.
A critical step in the initialization of the IDF is pop-
ulating the file-header keywords that describe the space-
craft’s orbit and control the subsequent actions of the
pipeline. The names of all calibration files to be used by
the pipeline are read from the file master calib file.dat
and written to file-header keywords. (Keywords for each
calibration file are included in the discussion that fol-
lows.) Three sets of calibration files are time-dependent:
the effective area is interpolated from the two files
with effective dates immediately preceding and follow-
ing the exposure start time (these file names are stored
4 Dixon et al.
in the header keywords AEFF1CAL and AEFF2CAL);
the scattered-light model is taken from the file with an
effective date immediately preceding the exposure start
time (keyword BKGD CAL); and the orbital elements
are read from the FUSE.TLE file, an ASCII file contain-
ing NORAD two-line elements for each day of the mis-
sion. These two-line elements are used to populate both
the orbital ephemeris keywords in the IDF file header and
the various spacecraft-position arrays in the timeline ta-
ble. Finally, a series of data-processing keywords is set
to either PERFORM or OMIT the subsequent steps of
the pipeline. Once a step is performed, the correspond-
ing keyword is set to COMPLETE. Some user control of
the pipeline is provided by the screening and parameter
files (SCRN CAL and BKGD CAL), which allow one,
for example, to select only night-time data or to turn
off background subtraction. An annotated list of file-
header keywords, including the calibration files used by
the pipeline, is provided in the FUSE Instrument and
Data Handbook.
Caveats: Occasionally, photon arrival times in raw
time-tag data files are corrupted. When this happens,
some fraction of the photon events have identical, enor-
mous TIME values, and the good-time intervals contain
an entry with START and STOP set to the same large
value. The longest valid exposure spans 55 ks (though
most are ∼ 2 ks long). If an entry in the GTI table ex-
ceeds this value, the corresponding entry in the timeline
table is flagged as bad (using the “photon arrival time
unknown” flag; § A-3). Bad TIME values less than 55 ks
will not be detected by the pipeline.
Raw histogram files may also be corrupted. OPUS fills
missing pixels in a histogram image with the value 21865.
The pipeline sets the WEIGHT of such pixels to zero and
flags them as bad (by setting the photon’s “fill-data bit”;
§ A-3). Occasionally, a single bit in a histogram image
pixel is flipped, producing (for high-order bits) a “hot
pixel” in the image. The pipeline searches for pixels with
values greater than 8 times the average of their neighbors,
identifies the flipped bit, and resets it.
One or more image extensions may be missing from a
raw histogram file (§A-2). If no extensions are present,
the keyword EXP STAT in the IDF header is set to −1.
Exposures with non-zero values of EXP STAT are pro-
cessed normally by the pipeline, but are not included in
the observation-level spectral files ultimately delivered to
MAST (§ 4.11). Though the file contains no data, the
header keyword EXPTIME is not set to zero.
Early versions of the CalFUSE pipeline did not make
use of the housekeeping files, but instead employed engi-
neering information downloaded every five minutes in a
special “engineering snapshot” file. That information is
used by OPUS to populate a variety of header keywords
in the raw data file. If a housekeeping file is not avail-
able, CalFUSE v3 uses these keywords to generate the
detector high-voltage and count-rate arrays in the time-
line table. Should these header keywords be corrupted,
the pipeline issues a warning and attempts to estimate
the corrupted values. In such cases, it is wise to compare
the resulting dead-time corrections (§ 4.3.2) with those
of other, uncorrupted exposures of the same target.
4.3. Convert to FARF
The pipeline module cf convert to farf is designed
to remove detector artifacts. Our goal is to construct
the data set that would be obtained with an ideal detec-
tor. The corrections can be grouped into two categories:
dead-time effects, which are system limitations that re-
sult in the loss of photon events recorded by the detec-
tor, and positional inaccuracies, i.e., errors in the raw
X and Y pixel coordinates of individual photon events.
The coordinate system defined by these corrections is
called the flight alignment reference frame, or FARF.
Corrected coordinates for each photon event are written
to the XFARF and YFARF arrays of the IDF.
4.3.1. Digitizer Keywords
The first subroutine of this module,
cf check digitizer, merely compares a set of 16
IDF file header keywords, which record various detector
settings, with reference values stored in the calibration
file DIGI CAL. Significant differences result in warning
messages being written to both the file header and the
exposure trailer file. Such warning messages should be
taken seriously, as data obtained when the detectors are
not properly configured are likely to be unusable. Be-
sides issuing a warning, the program sets the EXP STAT
keyword in the IDF header to −2.
4.3.2. Detector Dead Time
The term “dead time” refers specifically to the finite
time interval required by the detector electronics to pro-
cess a photon event. During this interval, the detector is
“dead” to incoming photons. The term is more generally
applied to any loss of data that is count-rate dependent.
There are three major contributions to the effective de-
tector dead time on FUSE. The first is due to limitations
in the detector electronics, which at high count rates may
not be able to process photon events as fast as they ar-
rive. The correction for this effect is computed separately
for each segment from the count rate measured at the
detector anode by the Fast Event Counter (FEC) and
recorded to the engineering data stream, typically once
every 16 seconds. The functional form of the correction
was provided by the detector development group at the
University of California, Berkeley, and its numerical con-
stants were determined from in-flight calibration data. It
is applied by the subroutine cf electronics dead time.
A second contribution to the dead time comes from
the way that the Instrument Data System (IDS) pro-
cesses counts coming from the detector. The IDS can
accept at most 8000 counts per second in time-tag mode
and 32000 counts per second in histogram mode from
the four detector segments (combined). At higher count
rates, photon events are lost. To correct for such losses,
the subroutine cf ids dead time compares the Active
Image Counter (AIC) count rate, measured at the back
end of the detector electronics, with the maximum al-
lowed rate. The IDS dead-time correction is the ratio of
these two numbers (or unity, whichever is greater).
A third contribution occurs when time-tag data are
bundled into 64 kB data blocks in the IDS bulk memory.
This memory is organized as a software FIFO (first-in,
first-out) memory buffer, and the maximum data transfer
rate from it to the spacecraft recorder (the FIFO drain
rate) is approximately 3500 events per second. At higher
count rates, the FIFO will eventually fill, resulting in the
CalFUSE: The FUSE Calibration Pipeline 5
loss of one or more data blocks. The effect appears as
a series of data drop-outs, each a few seconds in length,
in the raw data files. The correction, computed by the
subroutine cf fifo dead time, is simply the ratio of the
AIC count rate to the FIFO drain rate. When triggered,
this correction incorporates (and replaces) the IDS cor-
rection discussed above.
The total dead-time correction (always ≥ 1.0) is
simply the product of the detector electronics and
IDS corrections. It is computed (by the subroutine
cf apply dead time) once each second and applied to
the data by scaling the WEIGHT associated with each
photon event. The mean value of the detector electron-
ics, IDS, and total dead-time corrections are stored in
the DET DEAD, IDS DEAD, and TOT DEAD header
keywords, respectively. Other possible sources of dead
time, such as losses due to the finite response time of the
MCPs, have a much smaller effect and are ignored.
Caveats: Our dead-time correction algorithms are in-
appropriate for very bright targets. If the header key-
word TOT DEAD > 1.5, then the exposure should not
be considered photometric. If the housekeeping file for
a particular exposure is missing, the file header key-
words from which the count rates are calculated appear
to be corrupted, and either DET DEAD or IDS DEAD
is > 1.5, then the dead-time correction is assumed to be
meaningless and is set to unity. In both of these cases,
warning messages are written to the file header and the
trailer file.
4.3.3. Temperature-Dependent Changes in Detector
Coordinates
The X and Y coordinates of a photon event do not
correspond to a physical pixel on the detector, but
are calculated from timing and voltage measurements
of the incoming charge cloud (Siegmund et al. 1997;
Sahnow et al. 2000b). As a result, the detector coor-
dinate system is subject to drifts in the detector elec-
tronics caused by temperature changes and other effects.
To track these drifts, two signals are periodically injected
into the detector electronics. These “stim pulses” appear
near the upper left and upper right corners of each de-
tector, outside of the active spectral region. The stim
pulses are well placed for tracking changes in the scale
and offset of the X coordinate, but they are not well
enough separated in Y to track scale changes along that
axis. The subroutine cf thermal distort determines
the X and Y centroids of the stim pulses, computes the
linear transformation necessary to move them to their
reference positions, and applies that transformation to
the X and Y coordinates of each photon event in the re-
gions of the stim pulses and in the active region of the
detector. Events falling within 64 pixels (in X and Y)
of the expected stim-pulse positions are flagged by set-
ting the stim-pulse bit in the LOC FLGS array (§ A-3).
In raw histogram files, the stim pulses are stored in a
pair of image extensions. If either of these extensions is
missing, the pipeline reads the expected positions of the
stim pulses from the calibration file STIM CAL and ap-
plies the corresponding correction. This works (to first
order) because the stim pulses drift slowly with time,
though short-timescale variations cannot be corrected if
the stim pulses are absent.
4.3.4. Count-Rate Dependent Changes in Detector Y Scale
For reasons not yet understood, the detector Y scale
varies with the count rate, in the sense that the detector
image for a high count-rate exposure is expanded in Y. To
measure this effect, we tabulated the positions of individ-
ual detector features (particularly bad-pixel regions) as a
function of the FEC count rate (§ 4.3.2) and determined
the Y corrections necessary to shift each detector feature
to its observed position in a low count-rate exposure.
From this information, we derived the calibration file
RATE CAL for each detector segment. The correction
is stored as a two-dimensional image: the first dimension
represents the count rate and the second is the observed
Y pixel value. The value of each image pixel is the Y shift
(in pixels) necessary to move a photon to its corrected
position. The subroutine cf count rate y distort ap-
plies this correction to each photon event in the active
region of the detector. For time-tag data, the FEC count
rate is used to compute a time- and Y-dependent correc-
tion; for histogram data, the weighted mean of the FEC
count rate is used to derive a set of shifts that depends
only on Y.
4.3.5. Time-Dependent Changes in Detector Coordinates
As the detector and its electronics age, their proper-
ties change, resulting in small drifts in the computed
coordinates of photon events. These changes are most
apparent in the Lyman β airglow features observed in
each of the three apertures of the LiF and SiC channels
(Fig. 3), which drift slowly apart in Y as the mission pro-
gresses, indicating a time-dependent stretch in the detec-
tor Y scale. To correct for this stretch, the subroutine
cf time xy distort applies a time-dependent correction
(stored in the calibration file TMXY CAL) to the Y co-
ordinate of each photon event in the active region of the
detector.
Caveats: Although there is likely to be a similar change
to the X coordinate, no measurement of time-dependent
drifts in that dimension is available, so no correction is
applied.
4.3.6. Geometric Distortion
In an image of the detector generated from raw X and
Y coordinates, the spectrum is not straight, but wig-
gles in the Y dimension (Fig. 4). To map these geo-
metric distortions, we made use of two wire grids (the
so-called “quantum efficiency” and “plasma” grids) that
lie in front of each detector segment. Both grids are
regularly spaced and cover the entire active area of the
detectors. Although designed to be invisible in the spec-
tra, they cast sharp shadows on the detector when il-
luminated directly by on-board stimulation (or “stim”)
lamps. We determined the shifts necessary to straighten
these shadows. Their spacing is approximately 1 mm, too
great to measure fine-scale structure in the X dimension,
but sufficient for the Y distortion. Geometric distortions
in the X dimension have the effect of compressing and
expanding the spectrum in the dispersion direction, so
the X distortion correction is derived in parallel with the
wavelength calibration as described in § 5.1. The geomet-
ric distortion corrections are stored in a set of calibration
files (GEOM CAL) as pairs of 16384× 1024 images, one
each for the X and Y corrections. The value of each im-
6 Dixon et al.
age pixel is the shift necessary to move a photon to its
corrected position. This shift is applied by the subrou-
tine cf geometric distort.
Caveats: Though designed to be invisible, the wire
grids can cast shadows that are visible in the spectra of
astrophysical targets. These shadows are the “worms”
discussed in § 6.3.
4.3.7. Pulse-Height Variations in Detector X Scale
The FUSE detectors convert each ultraviolet photon
into a shower of electrons, for which the detector elec-
tronics calculate the X and Y coordinates and the to-
tal charge, or pulse height. Prolonged exposure to pho-
tons causes the detectors to become less efficient at
this photon-to-electron conversion (a phenomenon called
“gain sag”), and the mean pulse height slowly decreases.
Unfortunately, the X coordinate of low-pulse-height pho-
ton events is systematically miscalculated by the detec-
tor electronics. As the pulse height decreases with time,
spectral features appear to “walk” across the detector.
The strength of the effect depends on the cumulative
photon exposure experienced by each pixel and therefore
varies with location on the detector.
To measure the error in X as a function of pulse height,
we used data from long stim lamp exposures to construct
a series of 32 detector images, each containing events
with a single pulse height (allowed values range from
0 to 31). We stepped through each image in X, com-
puting the shift (∆X) necessary to align the shadow of
each grid wire with the corresponding shadow in a stan-
dard image constructed from photon events with pulse
heights between 16 and 20. The shifts were smoothed
to eliminate discontinuities and stored in calibration files
(PHAX CAL) as a two-dimensional image: the first di-
mension represents the observed X coordinate, and the
second is the pulse height. The value of each image
pixel is the walk correction (∆X) to be added to the
observed value of X. This correction, assumed to be in-
dependent of detector Y position, is applied by the sub-
routine cf pha x distort.
Caveats: For time-tag data, the walk correction is
straightforward and reasonably accurate. For histogram
data, pulse-height information is unavailable, so the
subroutine cf modify hist pha assigns to each photon
event the mean pulse height for that aperture, derived
from contemporaneous time-tag observations and stored
in the calibration file PHAH CAL. While this trick places
time-tag and histogram data on the same overall wave-
length scale, small-scale coordinate errors due to local-
ized regions of gain sag (e.g., around bright airglow lines,
particularly Lyman β) remain uncorrected in histogram
spectra.
4.3.8. Detector Active Region
When the IDF is first created, photon events with co-
ordinates outside the active region of the detector are
flagged as bad (§ 4.2). Once their coordinates are con-
verted to the FARF, the subroutine cf active region
flags as bad any photons that have been repositioned
beyond the active region of the detector. These limits
are read from the electronics calibration file (stored un-
der the header keyword ELEC CAL). Allowed values are
800 ≤ XFARF ≤ 15583, 0 ≤ YFARF ≤ 1023. The
active-area bit is written to the LOC FLGS array.
4.3.9. Uncorrected Detector Effects
CalFUSE does not perform any sort of flat-field correc-
tion. Pre-launch flat-field observations differ sufficiently
from in-flight data to make them unsuitable for this pur-
pose, and in-flight flat-field data are unavailable. (Even
if such data were available, any flat-field correction would
be only approximate, because MCPs do not have physical
pixels for which pixel-to-pixel variations can be clearly
delineated; § 4.3.3). As a result, detector fixed-pattern
noise limits the signal-to-noise ratio achievable in obser-
vations of bright targets. To the extent that grating,
mirror, and spacecraft motions shift the spectrum on the
detector during an exposure, these fixed-pattern features
may be averaged out. For some targets, we deliberately
move the FPAs between exposures to place the spectrum
on different regions of the detector. Combining the ex-
tracted spectra from these exposures can significantly im-
prove the resulting signal-to-noise ratio (§ 4.5.5). Other
detector effects (including the moiré pattern discussed in
§ 6.4) are described in the FUSE Instrument and Data
Handbook.
4.4. Screen Photons
The module cf screen photons calls subroutines de-
signed to identify periods of potentially bad data, such
as Earth limb-angle violations, SAA passages, and de-
tector bursts. A distinct advantage of CalFUSE v3 over
earlier versions of the pipeline is that bad data are not
discarded, but merely flagged, allowing users to mod-
ify their selection criteria without having to re-process
the data. To speed up processing, the pipeline calcu-
lates the various screening parameters once per second
throughout the exposure, sets the corresponding flags in
the STATUS FLAGS array of the timeline table, then
copies the flags from the appropriate entry of the time-
line table into the TIMEFLGS array for each photon
event (§ A-3). Many of the screening parameters applied
by the pipeline are set in the screening parameter file
(SCRN CAL). Other parameters are stored in various
calibration files as described below.
4.4.1. Airglow Events
Numerous geocoronal emission features lie within the
FUSE waveband (Feldman et al. 2001). While the
pipeline processes airglow photons in the same manner
as all other photon events in the target aperture, it is
occasionally helpful to exclude from consideration re-
gions of the detector likely to be contaminated by geo-
coronal or scattered solar emission. These regions are
listed in the calibration file AIRG CAL; the subroutine
cf screen airglow flags as airglow (by setting the air-
glow bit of the LOC FLGS array in the photon-event list)
all photon events falling within the tabulated regions –
even for data obtained during orbital night, when many
airglow features are absent.
4.4.2. Limb Angle
Spectra obtained when a target lies near the earth’s
limb are contaminated by scattered light from strong
geocoronal Lyman α and O I emission. To minimize this
effect, the subroutine cf screen limb angle reads the
LIMB ANGLE array of the timeline table, identifies pe-
riods when the target violates the limb-angle constraint,
CalFUSE: The FUSE Calibration Pipeline 7
and sets the corresponding flag in the STATUS FLAGS
array of the timeline table. Minimum limb angles for day
and night observations are read from the BRITLIMB
and DARKLIMB keywords of the screening parameter
file and copied to the IDF file header. The default limits
are 15◦ during orbital day and 10◦ during orbital night.
4.4.3. SAA Passages
The South Atlantic Anomaly (SAA) marks a depres-
sion in the earth’s magnetic field that allows particles
trapped in the Van Allen belts to reach low altitudes.
The high particle flux in this region raises the background
count rate of the FUSE detectors to unacceptable levels.
The subroutine cf screen saa compares the spacecraft’s
ground track, recorded in the LONGITUDE and LAT-
ITUDE arrays of the timeline table, with the limits of
the SAA (stored in the calibration file SAAC CAL as
a binary table of latitude-longitude pairs) and flags as
bad periods when data were taken within the SAA. Our
SAA model was derived from orbital information and
on-board counter data from the first three years of the
FUSE mission.
Caveats: Because the SAA particle flux is great enough
to damage the FUSE detectors, we end most exposures
before entering the SAA and lower the detector high volt-
age during each SAA pass. As a result, very little data
is actually flagged by this screening step.
4.4.4. Detector High Voltage
The detector high voltage is set independently for each
detector segment (1A, 1B, 2A, 2B). During normal op-
erations, the voltage on each segment alternates between
its nominal full-voltage and a reduced SAA level. The
SAA level is low enough that the detectors are not dam-
aged by the high count rates that result from SAA passes,
and it is often used between science exposures to mini-
mize detector exposure to bright airglow emission. The
full-voltage level is the normal operating voltage used
during science exposures. It is raised regularly to com-
pensate for the effects of detector gain sag. Without
this compensation, the mean pulse height of real photon
events would gradually fall below our detection thresh-
old. Unfortunately, there is a limit above which the
full-voltage level cannot be raised. Detector segment
2A reached this limit in 2003 and has not been raised
since; it will gradually become less sensitive as the frac-
tion of low-pulse-height events increases. The subroutine
cf screen high voltage reads the instantaneous value
of the detector high voltage from the HIGH VOLTAGE
array of the timeline table, compares it with the nominal
full-voltage level (stored as a function of time in the cali-
bration file VOLT CAL), and flags periods of low voltage
as bad.
For any number of reasons, an exposure may be ob-
tained with the detector high voltage at less than the
full-voltage level. To preserve as much of this data as pos-
sible, we examined all of the low-voltage exposures taken
during the first four years of the mission and found that,
for detector segments 1A, 1B, and 2B, the data quality is
good whenever the detector high voltage is greater than
85% of the nominal (time-dependent) full-voltage level.
For segment 2A, data obtained with the high voltage
greater than 90% of full are good, lower than 80% are
bad, and between 80 and 90% are of variable quality. In
this regime, the pipeline flags the affected data as good,
but writes warning messages to both the IDF header and
the trailer file. When this warning is present in time-tag
data, the user should examine the distribution of pulse
heights in the target aperture to ensure that the photon
events are well separated from the background (§ 4.4.12).
For histogram data, the spectral photometry and wave-
length scale are most likely to be affected.
Caveats: If the header keywords indicate that the de-
tector voltage was high, low, or changed during an ex-
posure, the IDF initialization routines (§ 4.2) write a
warning message to the trailer file. If a valid housekeep-
ing file is available for the exposure, this warning may
be safely ignored, because the pipeline uses housekeep-
ing information to populate the HIGH VOLTAGE array
in the timeline table and properly excludes time inter-
vals when the voltage was low. If the housekeeping file
is not present, each entry of the HIGH VOLTAGE array
is set to the “HV bias maximum setting” reported in the
IDF header. In this case, the pipeline has no information
about time-dependent changes in the detector high volt-
age, and warnings about voltage-level changes should be
investigated by the user.
4.4.5. Event Bursts
Occasionally, the FUSE detectors register large count
rates for short periods of time. These event bursts can
occur on one or more detectors and often have a complex
distribution across the detector, including scalloping and
sharp edges (Fig. 5). CalFUSE includes a module that
screens the data to identify and exclude bursts. The sub-
routine cf screen burst computes the time-dependent
count rate using data from background regions of the de-
tector (excluding airglow features) and applies a median
filter to reject time intervals whose count rates differ by
more than 5 standard deviations (the value may be set
by the user) from the mean. The algorithm rejects any
time interval in which the background rate rises rapidly,
as when an exposure extends into an SAA or the tar-
get nears the earth limb. The background rate com-
puted by the burst-rejection algorithm is stored in the
BKGD CNT RATE array of the timeline table and in-
cluded on the count-rate plots generated for each expo-
sure (§ 4.10). Burst rejection is possible only for data
obtained in time-tag mode.
Caveats: Careful examination of long background ob-
servations reveals that many are contaminated by emis-
sion from bursts too faint to trigger the burst-detection
algorithm. Observers studying, for example, diffuse
emission from the interstellar medium should be alert
to the possibility of such contamination.
4.4.6. Spacecraft Drift
Pointing of the FUSE spacecraft was originally con-
trolled with four reaction wheels, which typically main-
tained a pointing accuracy of 0.2–0.3 arc seconds. In late
2001, two of the reaction wheels failed, and it became
necessary to control the spacecraft orientation along one
axis with magnetic torquer bars. The torquer bars can
exert only about 10% of the force produced by the re-
action wheels, and the available force depends on the
strength and relative orientation of the earth’s magnetic
field. Thus, spacecraft drift increased dramatically along
this axis, termed the antisymmetric or A axis. Drifts
8 Dixon et al.
about the A axis shift the spectra of point-source tar-
gets in a direction 45◦ from the dispersion direction (i.e.,
∆X = ∆Y ). These motions can substantially degrade
the resolution of the spectra, so procedures have been
implemented to correct the data for spacecraft motion
during an exposure. For time-tag observations of point
sources, we reposition individual photon events. For his-
togram observations, we correct only for the exposure
time lost to large excursions of the spacecraft. The abil-
ity to correct for spacecraft drift became even more im-
portant when a third reaction wheel failed in 2004 De-
cember.
The correction of photon-event coordinates for space-
craft motion is discussed in § 4.5.7. During screening, the
subroutine cf screen jitter merely flags times when
the target is out of the aperture, defined as those for
which either ∆X or ∆Y , the pointing error in the disper-
sion or cross-dispersion direction, respectively, is greater
than 30′′, the width of the LWRS aperture. These lim-
its, set by the keywords DX MAX and DY MAX in the
CalFUSE parameter file (PARM CAL), underestimate
the time lost to pointing excursions, but smaller limits
can lead to the rejection of good data for some chan-
nels. Also flagged as bad are times when the jitter track-
ing flag TRKFLG = −1, indicating that the spacecraft
is not tracking properly. If TRKFLG = 0, no track-
ing information is available and no times are flagged as
bad. Pointing information is read from the jitter file
(JITR CAL; § A-2). If the jitter file is not present or the
header keyword JIT STAT = 1 (indicating that the jit-
ter file is corrupted), cf screen jitter issues a warning
and exits; again, no times are flagged as bad.
4.4.7. User-Defined Good-Time Intervals
One bit of the status array is reserved for user-defined
GTIs. For example, to extract data corresponding to a
particular phase of a binary star orbit, one would flag
data from all other phases as bad. A number of tools
exist to set this flag, including cf edit (available from
MAST). CalFUSE users may specify good-time inter-
vals by setting the appropriate keywords (NUSERGTI,
GTIBEG01, GTIEND01, etc.) in the screening pa-
rameter file. (Times are in seconds from the exposure
start time.) If these keywords are set, the subroutine
cf set user gtis flags times outside of these good-time
intervals as bad.
4.4.8. Time-Dependent Status Flags
Once the status flags in the timeline table are popu-
lated, the subroutine cf set photon flags copies them
to the corresponding entries in the photon event list. For
time-tag data, this process is straightforward: match the
times and copy the flags. Header keywords in the IDF
record the number of photon events falling in bad time in-
tervals or outside of the detector active area; the number
of seconds lost to bursts, SAAs, etc.; and the remaining
night exposure time. If more than 90% of the exposure
is lost to a single cause, an explanatory note is written
to the trailer file.
The task is more difficult for histogram data, for which
photon-arrival information is unavailable. We distin-
guish between time flags that represent periods of lost
exposure time (low detector voltage or target out of aper-
ture) and those that represent periods of data contami-
nation (limb angle violations or SAAs). For the former,
we need only modify the exposure time; for the latter,
we must flag the exposure as being contaminated. Our
goal is to set the individual photon flags and header key-
words so that the pipeline behaves in the following way:
When processing a single exposure, it treats all photon
events as good. When combining data from multiple
exposures, it excludes contaminated exposures (defined
below). To this end, we generate an 8-bit status word
containing only day/night information: if the exposure
is more than 10% day, the day bit is set. This status
word is copied onto the time-dependent status flag of
each photon event. We generate a second 8-bit status
word containing information about limb-angle violations
and SAAs: if a single second is lost to one of these events,
the corresponding flag is set and a message is written to
the trailer file. (To avoid rejecting an exposure that, for
example, abuts an SAA, we ignore its initial and final 20
seconds in this analysis.) The status word is stored in
the file header keyword EXP STAT (unless that keyword
has already been set; see § 4.2 and § 4.3.1). The routines
used by the pipeline to combine data from multiple ex-
posures into a single spectrum (§ 4.11) reject data files
in which this keyword is non-zero. The number of bad
events, the exposure time lost to periods of low detector
voltage or spacecraft jitter, and the exposure time dur-
ing orbital night are written to the file header, just as for
time-tag data.
Only in this subroutine is the DAYNIGHT keyword
read from the screening parameter file and written to the
IDF file header. Allowed values are DAY, NIGHT, and
BOTH. The default is BOTH. For most flags, if the bit
is set to 1, the photon event is rejected. The day/night
flag is different: it is always 1 for day and 0 for night.
The pipeline must read and interpret the DAYNIGHT
keyword before accepting or rejecting an event based on
the value of its day/night flag.
4.4.9. Good-Time Intervals
Once the time-dependent screening is complete, the
subroutine cf set good time intervals calculates a
new set of good-time intervals from information in the
timeline table and writes them to the second extension
of the IDF (§ 4.2). For time-tag data, all of the TIME-
FLGS bits are used and the DAYNIGHT filter is applied.
For histogram data, the bits corresponding to limb-angle
violations and SAAs are ignored, since data arriving dur-
ing these events cannot be excluded. The DAYNIGHT
filter is applied (assuming that all are day photons if the
exposure is more than 10% day). The exposure time,
EXPTIME = Σ (STOP−START), summed over all en-
tries in the GTI table, is then written to the IDF file
header.
4.4.10. Histogram Arrival Times
For histogram data, all of the photon events in an
IDF are initially assigned an arrival time equal to the
midpoint of the exposure. Should this instant fall in
a bad-time interval, the data may be rejected by a
subsequent step of the pipeline or one of our post-
processing tools. To avoid this possibility, the subroutine
cf modify hist times resets all photon-arrival times to
the midpoint of the exposure’s longest good-time inter-
val. This subroutine is not called for time-tag data.
CalFUSE: The FUSE Calibration Pipeline 9
4.4.11. Bad-Pixel Regions
Images of the FUSE detectors reveal a number of dead
spots that may be surrounded by a bright ring (see the
FUSE Instrument and Data Handbook for examples).
The subroutine cf screen bad pixels reads a list of
bad-pixel regions from a calibration file (QUAL CAL)
and flags as bad all photon events whose XFARF and
YFARF coordinates fall within the tabulated limits. A
bad-pixel map, constructed later in the pipeline (§ 4.8),
is used by the optimal-extraction algorithm to correct for
flux lost to dead spots.
4.4.12. Pulse Height Limits
For time-tag data, the pulse height of each photon
event is recorded in the IDF. Values range from 0 to
31 in arbitrary units. A typical pulse-height distribution
has a peak at low values due to the intrinsic detector
background, a Gaussian-like peak near the middle of the
range due to “real” photons, and a tail of high pulse-
height events, which likely represent the superposition of
two photons and therefore are not reliable. In addition,
the detector electronics selectively discard high pulse-
height events near the top and bottom of the detectors
(i.e., with large or small values of Y). We can thus im-
prove the signal-to-noise ratio of faint targets by rejecting
photon events with extreme pulse-height values. Pulse-
height limits (roughly 2–24) are defined for each detector
segment in the screening parameter file (SCRN CAL).
The subroutine cf screen pulse height flags photon
events with pulse heights outside of this range (by set-
ting the appropriate bit in the LOC FLGS array; § A-3)
and writes the pulse-height limits used and the number
of photon events rejected to the IDF file header. This
procedure is not performed on histogram data.
Caveats: We do not recommend the use of narrow
pulse-height ranges to reduce the detector background
in FUSE data. Careful analysis has shown that limits
more stringent than the default values can result in sig-
nificant flux losses across small regions of the detector,
particularly in the LiF1B channel, resulting in apparent
absorption features that are not real.
4.5. Remove Motions
Having corrected the data for various detector ef-
fects and identified periods of bad data, we continue
to work backwards through the instrument, correcting
for spectral motions on the detector due to the move-
ments of various optical components – and even of the
spacecraft itself. This task is performed by the module
cf remove motions. It begins by reading the XFARF
and YFARF coordinates of each photon event from the
IDF. It concludes by writing the motion-corrected coor-
dinates to the X and Y arrays of the same file.
4.5.1. Locate Spectra on the Detector
The LiF and SiC channels each produce three spec-
tra, one from each aperture, for a total of six spectra
per detector segment (Fig. 3). Because motions of the
optical components can shift these spectra on the detec-
tor, the first step is to determine the Y centroid of each.
To do this, we use the following algorithm: First, we
project the airglow photons onto the Y axis (summing
all values of X for each value of Y) and search the result-
ing histogram for peaks within 70 pixels of the expected
Y position of the LWRS spectrum. If the airglow fea-
ture is sufficiently bright (33 counts in 141 Y pixels), we
adopt its centroid as the airglow centroid for the LWRS
aperture and compute its offset from the expected value
stored in the CHID CAL calibration file. If the airglow
feature is too faint, we adopt the expected centroid and
assume an offset of zero. We apply the offset to the ex-
pected centroids of the MDRS and HIRS apertures to
obtain their airglow centroids. Second, we project the
non-airglow photons onto the Y axis and subtract a uni-
form background. Airglow features fill the aperture, but
point-source spectra are considerably narrower in Y and
need not be centered in the aperture. For each aperture,
we search for a 5σ peak within 40 pixels of the airglow
centroid. If we find it, we use its centroid; otherwise,
we use the airglow centroid. This scheme, implemented
in the subroutine cf find spectra, allows for the pos-
sibility that an astrophysical spectrum may appear in a
non-target aperture.
For each of the six spectra, two keywords are written
to the IDF file header: YCENT contains the computed
Y centroid, and YQUAL contains a quality flag. The
flag is HIGH if the centroid was computed from a point-
source spectrum, MEDIUM if computed from an airglow
spectrum, and LOW if the tabulated centroid was used.
It is possible for the user to specify the target centroid
by setting the SPEX SIC and SPEC LIF keywords in
the CalFUSE parameter file (PARM CAL). Two other
keywords, EMAX SIC and EMAX LIF, limit the offset
between the expected and calculated centroids: if the cal-
culated centroid differs from the predicted value by more
than this limit, the pipeline uses the default centroid.
Caveats: On detector 1, the SiC LWRS spectrum falls
near the bottom edge of the detector (Fig. 3). Because
the background level rises steeply near this edge, the cal-
culated centroid can be pulled (incorrectly) to lower val-
ues of Y, especially for faint targets.
4.5.2. Assign Photons to Channels
The subroutine cf identify channel assigns each
photon to a channel, where “channel” now refers to one
of the six spectra on each detector (Fig. 3). For each
channel, extraction windows for both point-source and
extended targets are tabulated in the calibration file
CHID CAL along with their corresponding spectral Y
centroids. These extraction limits encompass at least
99.5% of the target flux. For the target channels, iden-
tified in the APERTURE header keyword, we use either
the point-source or extended extraction windows, as indi-
cated by the SRC TYPE keyword; for the other (presum-
ably airglow) channels, we use the extended extraction
windows. The offset between the calculated and tabu-
lated spectral Y centroids (§ 4.5.1) is used to shift each
extraction window to match the data.
To insure that, should two extraction windows overlap,
photon events falling in the overlap region are assigned
to the more likely channel, photon coordinates (XFARF
and YFARF) are compared with the extraction limits
of the six spectral channels in the following order: first
the target channels (LiF and SiC); then the airglow chan-
nels (LiF and SiC) corresponding to the larger non-target
aperture; and finally the airglow channels (LiF and SiC)
10 Dixon et al.
corresponding to the smaller non-target aperture. For
example, if the target were in the MDRS aperture, the
search order would be MDRS LiF, MDRS SiC, LWRS
LiF, LWRS SiC, HIRS LiF, and HIRS SiC. The process
stops when a match is made. The channel assignment
of each photon event is stored in the CHANNEL array
(§ A-3); photon events that do not fall in an extraction
window are assigned a CHANNEL value of 0.
Channel assignment is performed twice, once before
the motion corrections and once after. The first time, all
extraction windows are padded by ±10 Y pixels to ac-
commodate errors in the channel centroids. The second
time, no padding is applied to time-tag data. Histogram
data, which are generally binned by 8 pixels in Y, present
a special challenge: The geometric correction (§ 4.3.6)
can move a row of data out of the extraction window,
producing a significant loss of flux. To prevent this, his-
togram extraction windows are padded by an additional
±8 Y pixels (or an amount equal to the binning factor
in Y, if other than 8).
4.5.3. Track Y Centroids with Time
For the LiF and SiC target apertures, the subroutine
cf calculate ycent motion computes the spectral Y
centroid as a function of time throughout the exposure.
The algorithm requires 500 photon events to compute
an average, so the centroid is updated more often for
bright targets than for faint ones. Photon events flagged
as airglow are ignored. The results are stored in the
YCENT LIF and YCENT SIC arrays of the timeline ta-
ble, but are not currently used by the pipeline. This cal-
culation is not performed for data obtained in histogram
mode.
4.5.4. Correct for Grating Motion
The FUSE spectrograph gratings are subject to small,
thermally-induced motions on orbital, diurnal, and pre-
cessional (60-day) timescales; an additional long-term,
non-periodic drift is also apparent. These motions can
shift the target and airglow spectra by as much as 15
pixels (peak to peak) in both the X and Y dimensions.
Measurements of the Lyman β airglow line in thousands
of exposures obtained throughout the mission reveal that
the gratings’ orbital motion depends on three parame-
ters: beta angle (the angle between the target and the
anti-sun vector), pole angle (the angle between the tar-
get and the orbit pole), and spacecraft roll angle (east of
north, stored in the file-header keyword APER PA). The
subroutine cf grating motion compares the beta, pole,
and roll angles of the spacecraft with a grid of values
in the calibration file GRAT CAL, reads the appropri-
ate correction, and computes the X and Y photon shifts.
The grating-motion correction is applied to all photon
events with CHANNEL > 0; photon events not assigned
to a channel are not moved.
Caveats: Some combinations of beta and pole angle
are too poorly sampled for us to derive a grating-motion
correction; for these regions, no correction is applied. At
present, only corrections for the orbital and long-term
grating motions are available. Because all photon events
in histogram data are assigned the same arrival time (the
midpoint of the longest good-time interval), they receive
the same grating-motion correction.
4.5.5. Correct for FPA Motion
The four focal-plane assemblies (shown in Fig. 1) can
be moved independently in either the X or Z direction.
FPA motions in the X direction are used to correct
for mirror misalignments and to perform FP splits (de-
scribed below). FPA motions in the Z direction are used
to place the apertures in the focal plane of the spectro-
graph. (Strictly speaking, an FPA moves along the tan-
gent to or the radius of the spectrograph Rowland circle,
not the X and Z axes shown in Fig. 1.) Both motions
change the spectrograph entrance angle, shifting the tar-
get spectrum on the detector. The FUSE wavelength
calibration is derived from a single stellar observation
obtained at a particular FPA position. The subroutine
cf fpa position computes the shift in pixels (∆X) nec-
essary to move each spectrum from its observed X posi-
tion on the detector to that of the wavelength-calibration
target. The X and Z positions of the LiF and SiC FPAs
are stored in file header keywords, various spectrograph
parameters are stored in a calibration file (SPEC CAL),
and the wavelength calibration and the FPA position
of the wavelength-calibration target are stored in the
WAVE CAL file. Shifts are computed for both the LiF
and SiC channels; the appropriate shift is applied to all
photon events with CHANNEL > 0; photon events not
assigned to a channel are ignored.
The FUSE detectors suffer from fixed-pattern noise.
Astigmatism in the instrument spreads a typical resolu-
tion element over several hundred detector pixels (pre-
dominantly in the cross-dispersion dimension), mitigat-
ing this effect, but to achieve a signal-to-noise ratio
greater than ∼ 30, one must remove the remaining fixed-
pattern noise. A useful technique is the focal-plane split.
FP splits are performed by obtaining a series of MDRS
or HIRS exposures at several FPA X positions. Moving
the FPA in the X dimension (and moving the satellite
to center the target in the aperture) between exposures
shifts both target and airglow spectra in the dispersion
direction on the detector. CalFUSE shifts each spectrum
to the standard X position expected by our wavelength
calibration routines. If the signal-to-noise ratio in the
spectra obtained at each FPA position is high enough, it
is possible for the user to disentangle the source spectrum
from the detector fixed-pattern noise; however, simply
combining extracted spectra obtained at different FPA
positions will average out most of the small-scale detec-
tor features.
4.5.6. Correct for Mirror Motion
The spectrograph mirrors are subject to thermal mo-
tions that shift the target’s image within the spectro-
graph aperture and thus its spectrum in both X and Y
on the detector. A source in either of the SiC channels
may move as much as 6′′ in a period of 2 ks. This motion
has two effects on the data: first, flux will be lost if the
source drifts (partially or completely) out of the aperture;
second, spectral resolution will be degraded (for LWRS
observations) as the spectrum shifts on the detector. Dif-
fuse sources, such as airglow emission, fill the aperture,
so their spectra are unaffected by mirror motion.
When the LiF1 channel is used for guiding, motions
of the LiF1 mirror are corrected by the spacecraft it-
self. Only the LiF2 and SiC spectra must be corrected
CalFUSE: The FUSE Calibration Pipeline 11
by CalFUSE. In theory, the switch from LiF1 to LiF2
as the primary channel for guiding the spacecraft (§ 2)
should require another set of calibration files. In practice,
the LiF2 mirror motion in the dispersion direction tracks
that of the LiF1 mirror. The mirror-motion correction is
stored as a function of time since orbital sunset (via the
TIME SUNSET array in the timeline table) in the cali-
bration file MIRR CAL. The correction (∆X) is applied
by the subroutine cf mirror motion to all photon events
within the target aperture; photon events in other aper-
tures and those not assigned to a channel are ignored.
This correction is not applied to extended sources. Be-
cause all photon events in histogram data are assigned
the same arrival time (generally the midpoint of the ex-
posure), they receive the same mirror-motion correction.
Caveats: We correct only the relative mirror motion
during an orbit, not the absolute mirror offset based on
longer-term trends. We do not correct for mirror motions
in the Y dimension. Finally, because the shifts for the
SiC1 and SiC2 mirrors are similar, we adopt a single
correction for both channels.
4.5.7. Correct for Spacecraft Motion
Spacecraft motions during an exposure shift the tar-
get spectrum on the detector and thus degrade spectral
resolution. The subroutine cf satellite jitter uses
pointing information stored in the jitter file (JITR CAL;
§ A-2) to correct the observed coordinates of photon
events for these motions. Pointing errors in arc sec-
onds are converted to X and Y pixel shifts and applied
to all photon events within the target aperture; events
in other apertures and those not assigned to a channel
are ignored. The correction is applied only if the jitter
tracking flag TRKFLG > 0, indicating that valid track-
ing information is available. TRKFLG values rise from
1 to 5 as the reliability of the pointing information in-
creases. The minimum acceptable value of the TRKFLG
may be adjusted by modifying the TRKFLG keyword in
the CalFUSE parameter file (PARM CAL).
4.5.8. Recompute Spectral Centroids
Once all spectral motions are removed from the data,
the subroutine cf calculate y centroid recomputes
the spectral Y centroids. Separate source and airglow
centroids are determined for each aperture in turn, from
largest to smallest. (The former is meaningless if the
aperture does not contain a source.) The offset be-
tween the measured airglow centroid in the LWRS aper-
ture and the tabulated centroid (from the calibration file
CHID CAL) is used to compute the airglow centroids for
the MDRS and HIRS apertures; the computed MDRS
and HIRS airglow centroids are ignored. The YCENT
value written to the IDF file header is determined by
the quality flag previously set by cf find spectra (§
4.5.1): if YQUAL = HIGH, the source centroid is used;
if YQUAL = MEDIUM, the airglow centroid is used; and
if YQUAL = LOW, the tabulated centroid is used. The
SPEX SIC, SPEC LIF, EMAX SIC, and EMAX LIF
keywords in the CalFUSE parameter file (PARM CAL)
have the effects discussed in § 4.5.1.
4.5.9. Final Assignment of Photons to Channels
The final assignment of each photon event to a chan-
nel is performed by cf identify channel, just as in §
4.5.2, but with two modifications: First, we consider
only photon events with CHANNEL > 0; unassigned
events (which are not motion corrected) remain unas-
signed. Second, we do not pad the extraction windows
by an additional ±10 pixels in Y, though the extraction
windows for histogram data are padded by ±8 Y pixels
(or an amount equal to the binning factor in Y, if other
than 8), as before.
4.5.10. Compute Count Rates
For time-tag data, cf target count rate computes
the count rate in the target aperture for the LiF and
SiC channels. To account for dead-time effects, the con-
tents of the WEIGHT array are used. Events in airglow
regions are excluded, but no other filters are applied to
the data. Results are written to the LIF CNT RATE
and SIC CNT RATE arrays of the timeline table. For
histogram data, the initial values of these arrays, taken
from the housekeeping file (§ A-3), are scaled by the value
of the header keyword DET DEAD.
4.6. Wavelength Calibration
Once converted to the FARF and corrected for optical
and spacecraft drifts, the data can be wavelength cal-
ibrated. The module cf assign wavelength performs
three tasks: first, it corrects for astigmatism in the spec-
trograph optics; second, it applies a wavelength calibra-
tion to each photon event; and third, it shifts the wave-
lengths to a heliocentric reference frame. The derivation
of the FUSE wavelength scale is discussed in § 5.1.
4.6.1. Astigmatism Correction
The astigmatic height of FUSE spectra perpendicular
to the dispersion axis is significant and varies as a func-
tion of wavelength (Fig. 3). Moreover, spectral features
show considerable curvature, especially near the ends of
the detectors where the astigmatism is greatest. The
subroutine cf astigmatism shifts each photon event in
X to correct for the spectral-line curvature introduced by
the FUSE optics, providing a noticeable improvement in
spectral resolution for point sources (Fig. 6).
The astigmatism correction is derived from observa-
tions of GCRV 12336, the central star of the Dumbbell
Nebula (M 27), which exhibits H2 absorption features
across the FUSE waveband. We cross-correlate and com-
bine the absorption features from a small range in X, fit
a parabola to each set of combined features, compute the
shift required to straighten each parabola, and interpo-
late the shifts across the waveband. Because an astigma-
tism correction has been derived only for point sources,
no correction is performed on the spectra of extended
sources, airglow spectra, or observations with APER-
TURE = RFPT.
The correction is stored in the calibration file
ASTG CAL as a two-dimensional image representing the
region of the detector containing the target spectrum. A
separate image is provided for each aperture. The value
of each image pixel is the astigmatism correction (∆X
in pixels) to be applied to that pixel. The entire image
is shifted in Y to match the centroid of the target spec-
trum, and the appropriate correction is applied to each
photon event in the target aperture. The corrected X co-
ordinates are not written to the IDF, but are passed im-
mediately to the wavelength-assignment subroutine. In
12 Dixon et al.
effect, we apply a two-dimensional wavelength calibra-
tion, which depends upon both the X and Y coordinates
of each photon event.
4.6.2. Assign Wavelengths
The wavelength calibration is stored as a binary table
extension in the calibration file WAVE CAL (§ 5.1); a
separate extension is provided for each aperture. Wave-
lengths are tabulated for integral values of X, assumed
to be in motion-corrected FARF coordinates. Given
the astigmatism-corrected X and CHANNEL arrays, the
subroutine cf dispersion considers each aperture in
turn and reads the corresponding calibration table. It
interpolates between tabulated values of X to derive the
wavelength of each photon event. Photon events not as-
signed to an aperture (CHANNEL = 0) are not wave-
length calibrated.
4.6.3. Doppler Correction
The component of the spacecraft’s orbital velocity
in the direction of the target is stored in the OR-
BITAL VEL array of the timeline table. The component
of the earth’s orbital velocity in the direction of the tar-
get is stored in the IDF file header keyword V HELIO.
Their sum is used to compute a time-dependent Doppler
correction, which is applied to each photon event by the
subroutine cf doppler and heliocentric. The result-
ing wavelength scale is heliocentric. Because histogram
data are assigned identical arrival times, their Doppler
correction is not time dependent. Histogram exposures
are kept short (approximately 500 seconds) to minimize
the resulting loss of spectral resolution. The final wave-
length assigned to each photon event is stored in the
LAMBDA array of the IDF.
4.7. Flux Calibration
Because the instrument sensitivity varies through the
mission, we employ a set of time-dependent effective-
area files (AEFF CAL). We interpolate between the two
files whose dates bracket the exposure start time but
do not extrapolate beyond the most recent effective-area
file. Within each calibration file, the instrumental ef-
fective area is stored as a binary table extension, with
a separate extension provided for each aperture. The
module cf flux calibrate invokes a single subroutine,
cf convert to ergs. Considering each aperture in turn,
the program reads the appropriate calibration files, inter-
polates between them if appropriate, and computes the
“flux density” of each photon (in units of erg cm−2) ac-
cording to the formula
ERGCM2 = WEIGHT× hc / LAMBDA / Aeff(λ), (1)
where ERGCM2, WEIGHT, and LAMBDA are read
from the photon-event list (§ A-3), h is Planck’s con-
stant, c the speed of light, and Aeff(λ) the effective area
at the wavelength of interest. Only photon events as-
signed to an aperture are flux calibrated; events with
CHANNEL = 0 are ignored. The flux density computed
for each photon event is stored in the ERGCM2 array of
the IDF. This array is not used by the pipeline, but is
employed by some of our interactive IDF manipulation
tools.
4.8. Create Bad-Pixel Map
When possible, spectra are extracted using an optimal-
extraction algorithm (§ 4.9.4) that employs a bad-pixel
map (BPM) to correct for flux lost to dead spots and
other detector blemishes. Because motions of the space-
craft and its optical components cause FUSE spectra
to wander on the detector, a particular spectral feature
may be affected by a dead spot for only a fraction of an
exposure. We thus generate a bad-pixel map for each
exposure. The module cf bad pixels reads a list of
dead spots from a calibration file (QUAL CAL), deter-
mines which of them overlap the target aperture, tracks
the motion corrections applied to each, and converts the
motion-corrected coordinates to wavelengths. The re-
sulting bad-pixel map (identified by the suffix “bpm.fit”)
has a format similar to that of an IDF (§ A-4), but the
WEIGHT column, whose values range from 0 to 1, rep-
resents the fraction of the exposure that each pixel was
affected by a dead spot. No BPM file is created if the
(screened) exposure time is less than 1 second. BPM files
are not archived, but can be generated from an IDF and
its associated jitter file using software distributed with
CalFUSE (available from MAST).
4.9. Extract Spectra
Through all previous steps of the pipeline, we resist
the temptation to convert the photon-event list into an
image. In the module cf extract spectra, we relent.
Indeed, we generate four sets of images: a background
model, a bad-pixel mask, a two-dimensional probabil-
ity distribution of the target flux, and a spectral image
for each extracted spectrum (LiF and SiC). Only pho-
ton events that pass all of the requested screening steps
(§ 4.4) are considered. If the (screened) exposure time
is less than 1 second or no photon events survive the
screening routines, then the program generates a null-
valued spectral file.
4.9.1. Background Model
Microchannel plates contribute to the detector back-
ground via beta decay of 40K in the MCP glass. On
orbit, cosmic rays add to this intrinsic background to
yield a total rate of ∼ 0.5 counts cm−2 s−1. Scattered
light, primarily geocoronal Lyman α, contributes a well-
defined illumination pattern (Fig. 7) that varies in inten-
sity during the orbit, with detector-averaged count rates
as small as 20% of the intrinsic background during the
night and 1-3 times the intrinsic rate during the day. We
assume that the observed background consists of three
independent components, a spatially uniform dark count
and spatially-varying day- and nighttime scattered-light
components. Properly scaling them to the data is thus a
problem with three unknown parameters. We attempt to
fit as many of these parameters as possible directly from
the data. When such a fit is not possible, we estimate
one or more components and fit the remainder.
Background events due to the detector generally have
pulse-height values lower than those of real photons (§
4.4.12). The observed dark count thus depends on the
pulse-height limits imposed on the data. An initial esti-
mate of the dark count, as a function of the lower pulse-
height threshold, is read from the background characteri-
zation file (BCHR CAL). The day and night components
CalFUSE: The FUSE Calibration Pipeline 13
of the scattered-light model are read from separate exten-
sions of the appropriate time-dependent background cal-
ibration file (BKGD CAL). The background models are
scaled to match the counts observed on unilluminated
regions of the detector. The Y limits of these regions
(selected according to the target aperture) are read from
header keywords in the IDF. Airglow photons in these
regions are excluded from the analysis. The day and
night counts in the background regions of the detector
are summed and recorded.
In its default mode, the subroutine cf scale bkgd es-
timates the uniform background as follows: The back-
ground regions of the day and night scattered-light mod-
els are scaled by their relative exposure times, summed,
and projected onto the X axis (to produce a histogram
in X). A similar histogram (called the “empirical back-
ground spectrum”) is constructed from the data. An it-
erative process is used to determine the uniform compo-
nent and scattered-light scale factor that best reproduce
the observed X distribution of background counts. The
uniform component is then subtracted from the day and
night totals computed above, and the day and night com-
ponents of the scattered-light model are scaled to match
the remaining observed counts.
If the empirical background spectrum is too faint, we
do not attempt to fit it, but assume that the uniform
component of the background is equal to the tabulated
dark-count rate scaled by the exposure time. The day
and night components of the scattered-light model are
calculated as above. Users who require a more accurate
background model may wish to combine data from mul-
tiple exposures before extracting a spectrum (see § 6.6).
If the empirical background spectrum is very bright –
as, for example, when nebular emission or a background
star contaminates one of the other apertures (and thus
the background-sample region) – no fit is performed. In-
stead, the day and night components of the scattered-
light model are scaled by the day and night exposure
times, the tabulated dark-count rate is scaled by the to-
tal exposure time, and the three components are summed
to produce a background image. This scheme is also used
for histogram data. Because histogram files contain data
only from the region about the desired extraction win-
dow, the background cannot be estimated from other
regions of the detector. Fortunately, histogram observa-
tions typically consist of short exposures of bright tar-
gets, so the background is comparatively faint.
Our day and night scattered-light models were de-
rived from the sum of many deep background observa-
tions spanning hundreds of kiloseconds. Individual ex-
posures that differ markedly from the mean were ex-
cluded from the sum. The data were processed only
through the FARF-conversion and data-screening steps
of the pipeline. Airglow features were replaced with a
mean background interpolated along the dispersion (X)
axis of the detector. An estimate of the uniform back-
ground component was subtracted from the final image,
and the data were binned by 16 detector pixels in X. This
process was performed on both day- and night-only data
sets. We produce a new set of background images every 6
to 12 months, as the effects of gain sag and adjustments
of the detector high voltage slowly alter the relative sen-
sitivity of the illuminated and background regions of the
detectors.
Caveats: While early versions of the pipeline (through
v2.4) assume a 10% uncertainty in the background flux,
propagating it through to the final extracted spectrum,
CalFUSE v3 treats uncertainties in the background as
systematic errors and does not include them in the
(purely statistical) error bars of the extracted spectra.
The algorithm assumes that the intensity of the uni-
form background is constant throughout an exposure.
This would be the case if it were due only to the detec-
tor dark count, but in fact the uniform background in-
cludes a substantial contribution from the scattered light
and is thus brighter during day-time portions of an expo-
sure. The assumption of a constant uniform background
can lead the algorithm to over-estimate the scale factors
for both the uniform and spatially-varying components
of the background model. A better scheme would be to
fit the day and night components of the uniform back-
ground separately. Similarly, when the empirical back-
ground spectrum is very faint, adopting the tabulated
value of the uniform background is not the best solu-
tion. It is likely that the scattered-light component of
the uniform background would be better estimated from
the observed day and night backgrounds. The difference
between the tabulated and observed levels of the uniform
background will become greater as the mission extends
into solar minimum and the intensities of both individ-
ual airglow features and the scattered-light component
of the uniform background continue to weaken.
Grating scattering of point-source photons along the
dispersion direction is potentially significant and is not
corrected by the CalFUSE pipeline. Typical values are
1–1.5% of the continuum flux in the SiC channels and 10
times less in the LiF channels.
4.9.2. Probability Array
The optimal-extraction algorithm (§ 4.9.4) requires as
input a two-dimensional probability array representing
the distribution of flux on the detector. Separate prob-
ability arrays, derived from high signal-to-noise stellar
observations, have been computed for each channel and
stored as image extensions in the weights calibration file
(WGTS CAL).
By construction, the Y dimension of the probability
array represents the maximum extent of the extraction
window for a particular aperture. For simplicity, all ar-
rays employed by the optimal-extraction algorithm are
trimmed to match the probability array in Y. The cen-
troids of the probability distribution and the target spec-
trum (recorded in the corresponding file headers) are
used to determine the offset between detector and prob-
ability coordinates. In the X dimension, all arrays are
binned to the output wavelength scale requested by the
user. Default wavelength parameters for each aperture
are specified in the header of the wavelength calibration
file (WAVE CAL); the default binning for all channels is
0.013 Å per output spectral bin, which corresponds to
approximately two detector pixels. The background ar-
ray, originally binned by 16 pixels in X, is rescaled to the
width of each output wavelength bin by the subroutine
cf rebin background. The probability array is rescaled
by the subroutine cf rebin probability array to have
a sum of unity in the Y dimension for each wavelength
14 Dixon et al.
4.9.3. Bad-Pixel Mask
A bad-pixel mask with the same wavelength scale and
Y dimensions as the probability array is constructed from
the BPM file (§ 4.8) by the subroutine cf make mask.
The array is initialized to zero. For each entry in the
BPM file, the value of the WEIGHT array is added to
the corresponding pixel of the bad-pixel mask. The mask
is then normalized and inverted, so that the center of the
deepest dead spot has a value of 0 and the regions outside
are set to 1. The conversion from pixel to wavelength
coordinates can open gaps in the mask, which appear as
values of unity surrounded by pixels with lower values.
We search for array elements that are larger than their
neighbors and replace them with the mean value of the
adjoining pixels. If the BPM file is absent or a particular
aperture is free of bad pixels, all elements of the bad-pixel
mask are set to 1.
4.9.4. Optimal (Weighted) Spectral Extraction
The extraction subroutine, cf optimal extraction,
is called separately for each of the two target spectra
(LiF and SiC). Inputs include the photon-event list and
the indices of events that pass through the various screen-
ings, as well as the 2-D background, probability, and bad-
pixel arrays described above. For numerical simplicity,
extraction is performed using the WEIGHT of each pho-
ton event, rather than its ERGCM2 value. A pair of 2-D
data and variance arrays with the same dimensions as the
probability array are constructed from the good photons
whose CHANNEL values correspond to the target aper-
ture. For time-tag data, this process is straightforward:
the LAMBDA and Y values of each photon event corre-
spond to a particular cell in the data and variance arrays.
That cell in the data array is incremented by the photon
weight, while the corresponding cell in the variance array
is incremented by the square of the weight. A 1-D raw-
counts spectrum (useful for the statistical analysis of low
count-rate data) is constructed simultaneously: for each
photon event added to the data array, the appropriate
bin of the counts spectrum is incremented by one.
For histogram data, the process is more complex, be-
cause the original detector image is generally binned by
8 pixels in Y and because each entry in the photon-event
list represents the sum of many individual photons. In
the Y dimension, an event’s WEIGHT is divided among
8 pixels (or the actual Y binning for that exposure, if
different) according to the distribution predicted by the
probability array. In the X dimension, each event is as-
sumed to have a width in wavelength space equal to the
mean dispersion per pixel for the channel (read from the
DISPAPIX keyword of the WAVE CAL file), and the
WEIGHT of an event that spans the boundary between
two output wavelength bins is divided between them.
This smoothing in X helps to mitigate the “beating” that
would otherwise occur between detector pixels and out-
put wavelength bins.
One-dimensional background, weights, and variance
spectra are then extracted from the two-dimensional
background, data, and variance arrays. To insure that
the three spectra sample the same region of the detector,
only cells in the 2-D arrays for which the correspond-
ing cell in the probability array has a value greater than
10−4 are included in the sum. These limits differ slightly
from those defined in the aperture (CHID CAL) calibra-
tion files. As a result, the ratio of the final weights and
counts spectra may not be a constant. (Ideally, their ra-
tio would equal the mean dead-time correction for the
exposure.) An initial flux spectrum, equal to the differ-
ence of the weights and background spectra, is used as
input to the optimal-extraction algorithm.
CalFUSE employs the optimal-extraction algorithm
described by Horne (1986), which requires as input the
2-D data, background, probability, and bad-pixel arrays
and the 1-D initial flux spectrum. Originally designed for
CCD spectroscopy, the algorithm has been modified for
the FUSE detectors. Specifically, instead of constructing
a 2-D spatial profile from each data set, we use a tabu-
lated probability array; the 2-D cosmic-ray mask, an in-
teger array in the original algorithm, is replaced with the
bad-pixel mask, which is a floating-point array; and the
2-D variance estimate is scaled by the bad-pixel mask.
Extraction is iterative: in the original version, iteration
is performed until the cosmic-ray mask stops changing.
In our version, iteration continues until the output flux
spectrum changes by less than 0.01 counts in all pixels. If
the loop repeats 50 times, the algorithm fails. The num-
ber of iterations performed is written to the OPT EXTR
keyword of the output file header.
If optimal extraction is successful, the variance of
the optimal spectrum is computed using the recipe of
Horne (1986). We have adapted this recipe to pro-
duce weights and background spectra such that FLUX
= WEIGHTS − BKGD. The resulting background spec-
trum is not smooth. Optimal extraction is not per-
formed on the spectra of extended sources or on those
for which the quality of the computed spectral centroid
is not HIGH. (Both the centroid and its quality flag are
stored in file header keywords.) In these cases, or if the
optimal-extraction algorithm fails, the initial flux, vari-
ance, weights, and background spectra are adopted.
However they are constructed, the final FLUX, ER-
ROR (equal to the square root of the variance),
WEIGHTS, and BKGD arrays (all in units of counts)
are returned to the calling routine. For time-tag data,
the COUNTS array as described above is returned. For
histogram data, the COUNTS array is computed by di-
viding the final WEIGHTS array by the mean dead-
time correction, which is stored in a file-header keyword
TOT DEAD. Also returned is the QUALITY array. It
is the product of the probability array and the bad-pixel
map, projected onto the wavelength axis and expressed
as an integer between 0 and 100. Its value is 0 if all the
flux in a wavelength bin is lost to a detector dead spot,
100 if no flux is lost.
Caveats: The optimal-extraction algorithm is designed
to improve the signal-to-noise ratio of the spectra of faint
point sources. Unfortunately, it is in precisely these cases
that the spectral centroid is most likely to be uncertain.
Because proper positioning of the probability array is es-
sential to the weighting scheme, observers of faint targets
may wish to combine the IDFs from multiple exposures
and re-compute their spectral centroids before attempt-
ing optimal extraction.
4.9.5. Extracted Spectral Files
Because the optimal-extraction routine returns the fi-
nal FLUX and ERROR arrays in units of counts, the
CalFUSE: The FUSE Calibration Pipeline 15
spectral-extraction module applies a flux calibration to
both arrays using the subroutine cf convert to ergs
described in § 4.7. Dividing each array element by (EX-
PTIME × WPC), where EXPTIME is the length in sec-
onds of the (screened) exposure and WPC the width in
Ångstroms of each output wavelength bin, completes the
conversion to units of erg cm−2 s−1 Å−1. The format of
the extracted spectral files is described in § A-5.
4.10. Trailer and Image Files
A number of supplementary files are generated by Cal-
FUSE and archived with the data. For each exposure
and detector segment, the pipeline generates a trailer file
and a pair of image files in Graphics Interchange Format
(GIF). The trailer file (suffix “.trl”) contains timing in-
formation for all pipeline modules and any warning or
error messages that they may have generated. The first
image file contains an image of the detector overlaid by a
wavelength scale and extraction windows for each aper-
ture (suffix “ext.gif”). Only photon events flagged as
good are included in the plot, unless there are none, in
which case all events are plotted. The second image file
presents count-rate plots for both the LiF and SiC target
apertures (suffix “rat.gif”). These arrays come from the
timeline table in the IDF and exclude photons flagged as
airglow. These image files are powerful tools for diagnos-
ing problems in the data, revealing, for example, when
high background levels cause the SiC1 LWRS extraction
window to be misplaced.
4.11. Observation-Level Files
For each exposure, CalFUSE produces LiF and SiC
spectra from each of four detector segments, for a total
of eight extracted spectral files. OPUS combines them
into a set of three observation-level files for submission
to MAST. Observation-level files are distinguished from
exposure-level files by having an exposure number of 000.
Depending on the target and the scientific questions at
hand, these files may be of sufficient fidelity for scientific
investigation. Here is a brief description of their contents:
ALL: For each combination of detector segment and
channel (LiF1A, SiC1A, etc.), we combine data from all
exposures in the observation into a single spectrum. If
the individual spectra are bright enough, we cross corre-
late and shift them before combining. (For each channel,
the shift calculated for the detector segment spanning
1000–1100 Å is applied to the other segment as well.) If
the spectra are too faint for cross correlation, we com-
bine the individual IDFs and extract a single spectrum
to optimize the background model. Combined spectra
(WAVE, FLUX, and ERROR arrays) for each of the eight
channels are stored in separate binary table extensions
in the following order: 1ALIF, 1BLIF, 2BLIF, 2ALIF,
1ASIC, 1BSIC, 2BSIC, and 2ASIC.
ANO (all, night-only): With the same format as the
ALL files, these spectra are constructed using only data
obtained during the night-time portion of each exposure.
They are generated only for time-tag data, and only if
EXPNIGHT > 0. The shifts calculated for the ALL files
are applied to the night-only data; they are not recom-
puted.
NVO (National Virtual Observatory): These files con-
tain a single spectrum spanning the entire FUSE wave-
length range. The spectrum is assembled by cutting and
pasting segments from the most sensitive channel at each
wavelength. Segments are shifted to match the guide
channel (either LiF1 or LiF2) between 1045 and 1070 Å.
Columns are WAVE, FLUX, and ERROR and are stored
in a single binary table extension.
The ALL file is used to generate a “quick-look” spec-
tral plot for each observation. When available, combined
spectra from channels spanning the FUSE waveband are
plotted in a single GIF image file (suffix “specttagf.gif”
or “spechistf.gif”). This plot appears on the MAST pre-
view page of each observation. Four additional GIF files
contain the combined LiF1, LiF2, SiC1, and SiC2 spectra
for each observation.
Caveats: Cross-correlation may fail, even for the spec-
tra of bright stars, if they lack strong spectral features.
Examples are nearby white dwarfs with weak interstellar
absorption lines. If cross-correlation fails for a given ex-
posure, that exposure is excluded from the sum. Thus,
the exposure time for a particular segment in an ALL
file may be less than the total exposure time for that
observation.
The cataloging software used by MAST requires the
presence of an ALL file for each exposure, not just for
the entire observation. We generate exposure-level ALL
files, but they contain no data, only a FITS file header.
The observation-level ALL files discussed above can be
distinguished by the string “00000all” in their names.
4.12. Quality Control and Archiving
Before the reduced data are archived, they undergo
a two-step quality-control process: First, a set of auto-
mated checks is performed on each exposure. The soft-
ware compares the flux observed in the guide channel
(LiF1 or LiF2) with that expected for the target and
with that observed in the other three channels. If an
anomaly is detected, a flag is set requesting manual in-
vestigation. The software works well for bright contin-
uum sources, but often flags faint or emission-line targets
as unsuccessful observations. Second, a member of the
FUSE operations team investigates any warnings gener-
ated by the software. If it is determined that less than
50% of the requested data were obtained, the target is
re-observed.
The philosophy of the FUSE project is to archive data
whenever possible, even if it does not satisfy the require-
ments of the original investigator. As a result, the MAST
archive contains a number of FUSE data sets that are in
some way flawed (e.g., misaligned channels, partial loss
of guiding, or no good observing time). Users should be
aware of this possibility.
If the pipeline detects an error in the data or its as-
sociated housekeeping or jitter files, it writes a warning
to both the trailer file and the headers of the IDF and
extracted spectral files. Users are advised to scan trailer
files for the “WARNING” string and spectral files for
“COMMENT” records. Occasionally, the FUSE opera-
tions team inserts comments directly into the headers of
raw data files. Such comments may warn of an unusual
instrument configuration, errors in the reported target
coordinates, or data obtained during slews.
Observation names beginning with the letter “S” are
science-verification observations and may have been ob-
tained with an unusual instrument configuration. For
16 Dixon et al.
example, the program S523 was designed to test pro-
cedures for observing a bright object by defocusing the
SiC mirrors. The LiF mirrors were moved for some S523
exposures. As a result, data from this program should
be used with caution. Abstracts for all FUSE observing
programs are available from MAST.
5. CALIBRATION FILES
5.1. Wavelength Calibration
5.1.1. Derivation of the FUSE Wavelength Calibration
Our principal wavelength-calibration target is
GCRV 12336, central star of the planetary nebula M 27,
whose spectrum exhibits a myriad of molecular-hydrogen
absorption features (McCandliss et al. 2007). For the
SiC1B channel, these data are supplemented at the
shortest wavelengths by spectra of the hot white dwarf
G 191-B2B (Lemoine et al. 2002). Spectra obtained
through each of the three FUSE apertures were fully
reduced, corrected for astigmatism, and used to derive
an empirical mapping of pixel to wavelength. For each
channel, standard optical expressions were used to
derive a theoretical dispersion solution, which was fit
to the empirical data with only its constant term (the
zero-point of the wavelength scale) as a free parameter.
The shifted theoretical dispersion solution was used to
generate the wavelength-calibration file.
Early versions of the pipeline relied on the wavelength
calibration to correct for non-linearities in the detector X
scale (the geometric distortion discussed in § 4.3.6). Cor-
recting for this effect separately has greatly improved the
accuracy of the FUSE wavelength scale. To determine
the geometric distortion in the X dimension, a spline
was fit to the residuals from each aperture (expected mi-
nus observed X coordinate of each absorption feature;
Fig. 8). In practice, residuals from all six apertures (both
LiF and SiC channels) were included in the fit, but data
from the other five apertures were weighted 100 times
less than those for the aperture being fitted. The addi-
tional data points help to constrain the fit in wavelength
regions where the data are sparse or missing. The spline
fits from all six apertures were then used to construct the
two-dimensional map of detector distortions in the X di-
mension that is used by the geometric-distortion routine
described in § 4.3.6. The process is iterative, with the
residuals (ideally) becoming smaller with each iteration.
The scatter of individual measurements about the
spline fit is caused in some cases by blended absorption
lines and in others by localized distortions induced by
the fiber-bundle structure of the MCPs. This scatter is
thus a fair estimate of the inaccuracies that the user may
expect in the relative measurement of the wavelength of
any given feature. The wavelength inaccuracies caused
by localized distortions are 3–4 detector pixels (0.025 Å
or 7 km s−1) at most wavelengths, but may be as large
as 6–8 pixels. They occur in tiny windows about 1 to 3 Å
wide, depending on the channel and segment. Some data
sets show larger residuals. These distortions are inherent
in the FUSE data set and represent the ultimate limit
to the accuracy of the FUSE wavelength calibration.
5.1.2. Zero-Point Uncertainties
The FUSE wavelength calibration assumes that the
motion-corrected spectrum falls at a precise location on
the detector. If it is shifted in X, then the wavelength
scale of the extracted spectrum will suffer a zero-point
offset. For the guide channel (either LiF1 or LiF2),
the dominant source of wavelength errors is thermally-
induced rotations of the spectrograph gratings, which de-
pend on the satellite attitude. For the other channels,
additional wavelength errors come from mirror misalign-
ments that shift the target away from the center of the
aperture. Such misalignments may produce zero-point
offsets of up to ±0.15 Å for point sources in the LWRS
aperture. Offsets are less than ±0.02 Å for the MDRS
aperture and are negligible for the HIRS aperture.
We define the zero point of our wavelength scale by
requiring that the Lyman β airglow feature, observed
through the HIRS aperture and processed as if it were a
point source, be at rest in spacecraft coordinates when all
Doppler corrections are turned off. The use of an airglow
feature eliminates errors due to mirror motions in the
non-guide channels, but not errors in the grating-motion
correction, so we measure the Lyman β line in some 200
background exposures and shift their mean velocity to
0 km s−1. For each channel, all three apertures (HIRS,
MDRS, and LWRS) use this HIRS-derived wavelength
scale (WAVE CAL version 022 and greater).
The grating-motion correction (§ 4.5.4) is designed to
place the centroid of each Lyman β airglow feature at
a fixed location in FARF coordinates. On average, it
achieves that goal: for our sample of 200 background ex-
posures, the measured velocity of the Lyman β line has a
standard deviation of between 2 and 3 km s−1, depend-
ing on the channel. Unfortunately, some combinations
of pole and beta angle are not well corrected, leading
to velocity offsets of 10 km s−1 or more, and additional
motions – of either the gratings or some other optical
component – can shift the extracted spectra by several
km s−1 from one exposure to the next.
Figure 9 presents the measured wavelength of the in-
terstellar O I λ1039 absorption feature in 47 exposures of
the hot white dwarf KPD 0005+5106 obtained through
the HIRS aperture. The 2001 and 2002 data show little
scatter and yield a mean velocity of −10.7± 1.9 km s−1.
(Holberg, Barstow, and Sion 1998 report a heliocentric
velocity of −7.50 ± 0.76 km s−1 for the interstellar fea-
tures along this line of sight.) The 2003 data (all from
a single observation) span nearly 10 km s−1. The 2004
data (again from a single observation) are tightly corre-
lated but offset by∼ 7 km s−1. These data were obtained
at a spacecraft orientation (beta and pole angles) that is
generally well corrected by our grating-motion algorithm;
apparently, some other effect is at work. The 2006 data
come from the LiF2 channel, which became the default
guide channel in 2005 (§ 6.1).
We do not recommend the general use of airglow lines
to fix the absolute wavelength scale of point-source spec-
tra for several reasons: First, airglow emission fills the
aperture, so the resulting airglow lines provide no in-
formation about the position of the target relative to
the aperture center. Second, the jitter correction (for all
channels) and the mirror-motion correction (for the SiC
channels) are inappropriate for airglow emission. Third,
the Doppler correction for the spacecraft’s orbital motion
can degrade their resolution.
CalFUSE: The FUSE Calibration Pipeline 17
TABLE 1
Stellar Parameters Adopted for FUSE
Flux Standards
Teff log g Vrad
Name (K) (cm s−2) (km s−1)
GD 71 32,843 7.783 80.0
GD 659 35,326 7.923 33.0
GD 153 39,158 7.770 50.0
HZ 43 50,515 7.964 20.6
GD 246 53,000 7.865 −13.2
G 191-B2B 61,200 7.5 · · ·
Note. — For G 191-B2B, we use the model
employed by Kruk et al. 1999 for the final
Astro-2 calibration of HUT.
5.1.3. Diffuse Emission
The FUSE wavelength scale is derived from
astigmatism-corrected, point-source spectra. Extended-
source (diffuse) spectra are not corrected for astig-
matism. If point-source data are processed with the
astigmatism correction turned off, the resulting wave-
length errors are less than about 4 detector pixels,
consistent with the uncertainties in the wavelength
scale. Therefore, the present FUSE wavelength calibra-
tion should be adequate for extended-source spectra.
Airglow lines are useful for determining the zero-point
for extended sources that fill the aperture.
5.2. Flux Calibration
5.2.1. Derivation of the Effective-Area Curve
The FUSE flux calibration is based on in-flight ob-
servations of the well-studied DA (pure-hydrogen) white
dwarfs listed in Table 1, which have been observed at reg-
ular intervals throughout the mission. For each channel,
data from multiple stars are combined to track changes
in the instrument sensitivity using a technique similar
to that developed by Massa and Fitzpatrick (2000) for
the International Ultraviolet Explorer (IUE)/ satellite.
The algorithm yields a series of time- and wavelength-
dependent sensitivity curves as well as the spectrum of
each star, in units of raw counts, as it would have ap-
peared on a date early in the mission, which we choose
to be T0 = 1999 December 31. (We refer to the latter as
“T0 spectra.”)
For each star, we generated a synthetic spectrum using
the programs TLUSTY (version 200) and SYNSPSEC
(version 48) of Hubeny and Lanz (1995). The non-LTE
pure-hydrogen model atmospheres were computed ac-
cording to a prescription by Hubeny (private commu-
nication) using 200 atmospheric layers to ensure an opti-
mal absolute flux accuracy. The atmospheric parameters
listed in Table 1, consistent with HST, IUE, and opti-
cal observations, were used to compute the models (Hol-
berg, private communication). For G 191-B2B, we used
the model employed by Kruk et al. (1999) for the final
Astro-2 calibration of HUT. Observations of these stars
with the Faint Object Spectrograph aboard HST have
shown that the models, including parameter uncertain-
ties, are consistent to within 2% at wavelengths longer
than Lyman α (Bohlin, Colina, and Finley 1995; Bohlin
1996). Uncertainties in the far-ultraviolet waveband are
slightly higher, as discussed by Kruk et al. (1999).
For each channel, the effective area in units of cm2 is
computed by dividing one or more T0 spectra in units of
counts s−1 Å−1 by a synthetic white-dwarf spectrum in
units of photons cm−2 s−1 Å−1. We find excellent agree-
ment between the effective areas derived from the differ-
ent standard stars. Sensitivity curves for the LiF1A and
SiC1A channels are presented in Fig. 10. (Effective-area
curves for all FUSE channels are available from MAST.)
The sensitivity of the LiF1A channel decreased by ∼ 15%
over the first three years of the mission, but appears to
have stabilized; that of the SiC1 channel has declined by
∼ 45% since launch and is falling still (though slowly).
Effective-area curves (AEFF CAL) for each channel and
detector segment were generated at three-month inter-
vals until the loss of the third reaction wheel in 2004
December; we plan to generate them at six-month inter-
vals for the duration of the mission.
Caveats: We do not attempt to correct spectra ob-
tained through the MDRS and HIRS apertures for
changes in instrument sensitivity, but employ a single
effective-area curve for each. The low throughput of
these apertures, combined with the likelihood that their
spectra are non-photometric, makes tracking changes in
their sensitivity both more difficult and less useful than
for the LWRS aperture.
5.2.2. Systematic Uncertainties
The greatest uncertainties in a line or continuum flux
derived from a FUSE spectrum are due to systematic
effects. An estimate of the uncertainty in our flux cali-
bration can be obtained by comparing the effective-area
curves derived from different white-dwarf stars. Differ-
ences among the curves reflect errors in both the model
atmospheres and the stellar parameters upon which they
are based. In most channels, the scatter in the derived
effective areas is between 2 and 4%.
The photometric accuracy of FUSE spectra is subject
to numerous effects that cannot be fully corrected by the
CalFUSE pipeline. A target centered in an aperture of
the guide channel (LiF1 or LiF2) may not be centered
in the corresponding apertures of the other three chan-
nels. Since the loss of the first two reaction wheels in
2001, spacecraft drifts may move the target out of even
the guide-channel aperture. While the pipeline does at-
tempt to flag times when the target is out of the aper-
ture, the algorithm used is conservative in that it un-
derestimates the time lost to pointing errors (§ 4.4.6).
The user is advised to consult the count-rate plots gen-
erated by the pipeline (suffix “rat.gif”; § 4.10) and the
LIF CNT RATE and SIC CNT RATE arrays of the IDF
timeline table to determine the photometric quality of an
exposure. Using tools available from MAST or the user-
defined good-time intervals discussed in § 4.4.7, users can
reject time periods when the count rate is low or re-scale
the flux of low-count-rate exposures.
When a point-source target falls near the top or bottom
edge of an aperture, vignetting in the spectrograph may
attenuate the target flux in a wavelength-dependent way.
Astigmatism gives FUSE spectra the shape of a bow tie
(Fig. 3). If vignetting is important, then the spectrum
will lie below the center of the aperture on one side of
the bow tie and above it on the other. Significant flux
loss is possible in wavelength regions far from the center
of the bow tie.
18 Dixon et al.
Other systematic uncertainties are imposed by various
detector flat-field effects; their relative importance de-
pends upon one’s scientific goals. For narrow emission
lines, flux uncertainties are dominated by the moiré pat-
tern (high-frequency ripples due to beating among the
arrays of microchannel pores in the MCP stack; § 6.4),
unless the observation was obtained using an FP split or
the equivalent was achieved via grating and mirror mo-
tions. For broad features, the moiré is not important,
but larger-scale flat-field features are. These effects are
discussed in The FUSE Instrument and Data Handbook.
Finally, when fitting a spectral energy distribution, the
greatest uncertainty is caused by worms (§ 6.3), which
may depress the observed flux over tens of Ångstroms by
50% or more.
5.2.3. Extended Sources
The FUSE flux calibration is derived from point-
source targets. Because the distribution of flux in the
cross-dispersion direction differs for point and extended
sources, it is possible that the instrumental sensitivity
may also differ; this question has not been explored in
detail. Extended spectra are less affected by worms (§
6.3) than are point-source spectra. Moreover, because
the spectrum of a diffuse emitter is spread over a larger
region of the detector, it will suffer less from local flat-
field effects.
6. DISCUSSION
6.1. Spacecraft Guiding on the LiF2 Channel
The switch from FES A to FES B as the default guide
camera in 2005 July has two principal effects on the qual-
ity of FUSE data. First, tracking with FES A ensured
that targets remained in the center of the LiF1 aperture,
which is the most sensitive channel in the astrophysically-
important 1000–1100 Å waveband. Tracking with FES
B will keep targets centered in the LiF2 aperture, in-
creasing the likelihood of data loss in the LiF1 channel.
Second, in order to optimize the optical focus of FES B,
the LiF2 FPA was moved out of the focal plane of the
LiF2 primary mirror. Observations of point sources with
the LWRS aperture are unaffected, and the point-source
spectral resolution of this channel is unchanged, but the
throughput of the narrow LiF2 apertures is reduced. The
effective transmission of the apertures has not been char-
acterized in detail, but is approximately 70% for LIF2
MDRS and 15% for LiF2 HIRS, versus 98% and 60% for
their LiF1 counterparts. The spectral resolution for dif-
fuse sources is expected to be slightly lower in LiF2 than
in LiF1.
6.2. Scattered Solar Emission
In addition to airglow lines, scattered solar emission
features are present in the SiC channels when observ-
ing at high beta angles during the sunlit portion of the
orbit. Emission from C III λ977.0, Lyman β λ1025.7,
and O VI λλ1031.9, 1037.6 has been positively identified.
Emission from N III λ991.6 and N II λ1085.7 may also
be present. It is believed that sunlight is scattered by
reflective, silver-coated Teflon blankets lying above the
SiC baffles. At low beta angles, scattered solar emission
is less apparent, because the blankets are shaded by the
SiC baffles and the open baffle doors and because the ra-
diation strikes the blankets at a high angle of incidence.
It is unknown at which beta angle, if any, the solar emis-
sion completely disappears. Because the LiF channels
lie on the shadowed side of the spacecraft, solar emission
lines are not seen in LiF spectra. C III and O VI emission
observed in the SiC channels during orbital day should
always be compared with the emission observed either
with the LiF channel or during the nighttime portion of
an orbit.
Since the failure of the third reaction wheel in 2004
December, FUSE mission controllers have experimented
with the use of non-standard roll angles to improve space-
craft stability. These roll angles can place the spacecraft
in a configuration that greatly increases the sunlight scat-
tered into one of the SiC channels. The scattered light,
mostly Lyman continuum emission, appears as an in-
crease in the background at wavelengths shorter than
about 920 Å; strong, resolved Lyman lines are present at
longer wavelengths. When present, it is generally seen
in only one of the two SiC channels. We have no way to
model or subtract this emission.
6.3. The Worm
The spectra of point-source targets occasionally exhibit
a depression in flux that may span as much as 50 Å
(Fig. 11). These depressions appear in detector images
as narrow stripes roughly parallel to the dispersion axis
(Fig. 12). The stripes, known as worms, can attenuate
as much as 50% of the incident light in affected portions
of the spectrum. Worms shift in the dispersion direction
when the target moves in the aperture. They are due
to an unfortunate interaction between the horizontal fo-
cus of the spectrograph and the innermost wire grid (the
quantum-efficiency grid; § 4.3.6). Since the location of
this focus point is a function of wavelength, the strength
of a worm is exquisitely sensitive to the exact position of
the spectrum on the detector. We cannot determine this
position with sufficient precision to correct reliably for
flux lost to worms. Though most prominent in LiF1B
LWRS spectra, worms can appear in all channels and
apertures. Observers who require absolute spectropho-
tometry should carefully examine FUSE spectral image
files for the presence of worms. The redundant wave-
length coverage of the various FUSE channels can be
used to mitigate their effects.
6.4. The Moiré Pattern in Histogram Data
Since the release of CalFUSE v3.0, users have reported
strong, non-Gaussian noise in the spectra of some bright
stars observed in histogram mode. An example is shown
in Fig. 13. The high-frequency ripples have a period of
approximately 9 detector pixels, or about 0.06 Å. These
ripples are a moiré pattern due to beating among the
arrays of microchannel pores in the three layers of the
MCP stack (Tremsin et al. 1999). The moiré fringes are
strongest on segment 2B, but are also visible on segments
1A and 2B. The motion corrections applied to time-tag
data tend to smooth out this effect, but it can be quite
strong in histogram data. Where it is present, users are
advised to smooth or bin their spectra by at least one
resolution element to reduce its effects. This and other
detector artifacts are described in the FUSE Instrument
CalFUSE: The FUSE Calibration Pipeline 19
and Data Handbook.
6.5. A Note about Time
The FUSE spacecraft uses Coordinated Universal
Time (UTC). The spacecraft clock is updated periodi-
cally from the ground using a procedure that corrects
for the signal transit time from the ground station to the
spacecraft. The ground station time comes from GPS
satellites. The Instrument Data System receives a 1 Hz
signal from the spacecraft that is used to align the IDS
clock with the spacecraft clock to an accuracy of ±5 ms.
In time-tag mode, the IDS typically inserts a time stamp
into the data stream once per second, but can insert time
stamps as frequently as 125 times per second. Unfortu-
nately, the binary format of the time stamp rounds the
time value to the nearest 1/128 of a second. The two pe-
riods beat against one another, causing the loss of three
time stamps each second. Additional timing uncertain-
ties due to delays in the detector electronics have not
been measured, but are assumed to be on the order of
a few milliseconds. For most time-tag observations, for
which time stamps are recorded only once per second,
these effects can safely be ignored.
Raw time-tag files are constructed by assigning the
value of the most recent time stamp, in units of seconds
from the exposure start time, to each subsequent photon
event. The frequency of these time markers determines
the temporal resolution of the data. Photon-arrival times
are not modified by the pipeline: values are UTC as as-
signed by the IDS. In particular, photon-arrival times are
not converted to a heliocentric scale.
6.6. Combining Data from Multiple Exposures
For each FUSE observation, OPUS combines data
from individual exposures into a set of observation-level
spectra, as described in § 4.11. While these files are suffi-
cient for many projects, other projects may benefit from
specialized data processing. Here are some points to keep
in mind when combining FUSE data from multiple expo-
sures: For bright targets, the goal is to maximize spectral
resolution, so it is important to align precisely the spectra
from individual exposures before combining them. The
wavelength zero points of segments A and B are consis-
tent across each of the FUSE detectors (§ 5.1), so shifts
measured for one detector segment can safely be applied
to the other. For observations made before 2005 July, the
LiF1 spectrum is likely to have the most accurate wave-
length scale, so it serves as the standard for the other
three channels. For later observations, the LiF2 spectra
are likely to be the most accurate. A procedure to cross-
correlate and shift spectra by hand is described in The
FUSE Data Analysis Cookbook. When cross-correlating
the spectra of point-source targets, it is important to ex-
clude regions contaminated by airglow features, as their
motions are unlikely to track those of the target. For
faint targets, the goal is to optimize the fidelity of the
background model by maximizing the signal-to-noise ra-
tio on background regions of the detector, a goal achieved
by combining the IDFs from multiple exposures before
extracting the spectra. A variety of C- and IDL17-based
tools to perform these and other data-analysis tasks has
17 IDL is a registered trademark of ITT Corporation for their
Interactive Data Language software.
TABLE 2
Format of Raw Time-Tag Files
Array Name Format Description
Primary Header-Data Unit (HDU 1)
Header only. Keywords contain exposure-specific information.
HDU 2: Photon Event List
TIME FLOAT Photon arrival time (seconds)
X SHORT Raw X position (0–16383)
Y SHORT Raw Y position (0–1023)
PHA BYTE Pulse height (0–31)
HDU 3: Good-Time Intervals
START DOUBLE GTI start time (seconds)
STOP DOUBLE GTI stop time (seconds)
Note. — Times are relative to the exposure start time, stored
in the header keyword EXPSTART.
been generated by the FUSE project. Software and doc-
umentation are available from MAST.
We acknowledge with gratitude the efforts of those who
contributed to the design and implementation of initial
versions of the CalFUSE pipeline and its associated cal-
ibration files: G. A. Kriss, E. M. Murphy, J. Murthy,
W. R. Oegerle, and K. C. Roth. This research has made
use of the Multimission Archive at the Space Telescope
Science Institute (MAST). STScI is operated by the As-
sociation of Universities for Research in Astronomy, Inc.,
under NASA contract NAS5-26555. Support for MAST
for non-HST data is provided by the NASA Office of
Space Science via grant NAG5-7584 and by other grants
and contracts. This work is supported by NASA contract
NAS5-32985.
Facility: FUSE
APPENDIX
A. FILE FORMATS
All FUSE data are stored as FITS files (Hanisch et al.
2001) containing one or more Header + Data Units
(HDUs). The first is called the primary HDU (or
HDU 1); it consists of a header and an optional N-
dimensional image array. The primary HDU may be fol-
lowed by any number of additional HDUs, called “exten-
sions.” Each extension has its own header and data unit.
FUSE employs two types of extensions, image extensions
(a 2-dimensional array of pixels) and binary table exten-
sions (rows and columns of data in binary representa-
tion). CalFUSE uses the CFITSIO subroutine library
(Pence 1999) to read and write FITS files.
A-1. Raw Time-Tag and Histogram Files
FUSE raw data files are generated by OPUS using
both data downlinked by the telescope and information
from the FUSE Mission Planning Database (§ 4.1). In-
formation regarding the target, exposure times, instru-
ment configuration, and engineering parameters is stored
in a series of header keywords in the primary HDU. All
header keywords are described in the FUSE Instrument
and Data Handbook. In raw time-tag files (Table 2), the
20 Dixon et al.
primary HDU consists of a header only, with no asso-
ciated image array. HDU 2 contains the photon-event
list, with arrival time (in seconds from the exposure start
time), raw detector coordinates, and pulse height for each
event in turn. HDU 3 lists good-time intervals (GTIs)
calculated by OPUS. Raw time-tag file names end with
the suffix “ttagfraw.fit.” They can be as large as 10–20
MB for the brightest targets.
The data in raw histogram files (suffix “histfraw.fit”)
are stored as a series of image extensions (Table 3).
The primary HDU contains the same header keywords
as time-tag files, along with a small (8 × 64 pixel) im-
age called the Spectral Image Allocation (SIA) table.
The SIA table is used to map regions of the detector
to on-board memory. Each element in the SIA table
corresponds to a 2048 × 16 pixel region on a detector
segment. If the element is set to 1, the photons from
the corresponding region are saved; if 0, they are dis-
carded. Additional image extensions follow, each con-
taining the binned image of some region of the detector;
these regions may overlap. In general, science data are
binned by 8 pixels in Y and unbinned in X; binning fac-
tors for each exposure are stored in the header keywords
SPECBINX and SPECBINY. While the format given in
Table 3 is standard, any number of image extensions may
be present in a histogram file. Raw histogram data files
are 1–1.5 MB in size.
A-2. Housekeeping and Jitter Files
For each exposure, a single housekeeping file is gen-
erated by OPUS from engineering data supplied by the
spacecraft (§ 4.1). Housekeeping files (suffix “hskpf.fit”)
contain 62 arrays, including spacecraft pointing informa-
tion, detector voltage levels, and various counter values,
in a single binary table extension. Arrays are tabulated
once per second, though most parameters are updated
only once every 16 seconds. Only a few of the house-
keeping arrays are employed by the pipeline. The detec-
tor high voltage and LiF, SiC, FEC, and AIC counter
arrays are used to populate the corresponding arrays in
the IDF timeline table (§ A-3).
From pointing information in the housekeeping file,
OPUS derives a jitter file (suffix “jitrf.fit”) consisting of
a single binary table extension with 4 columns: TIME,
DX, DY and TRKFLG. The time refers to the elapsed
time (in seconds) from the start of the exposure. Since
the engineering data commonly begin up to a minute be-
fore the exposure, the first few entries of this array are
negative. DX and DY are the offsets along the X (disper-
sion) and Y (cross-dispersion) directions in arc seconds.
These offsets are defined relative to the commanded posi-
tion of the telescope (presumably the target coordinates).
Finally, TRKFLG is the tracking quality flag. Its value
is −1 if the spacecraft is not tracking properly and 0 if
tracking information is unavailable. Values between 1
and 5 represent increasing levels of fidelity for DX and
Additional details regarding the contents and format
of the housekeeping and jitter files are provided in The
FUSE Instrument and Data Handbook.
A-3. Intermediate Data File (IDF)
The IDF (suffix “idf.fit”) contains three FITS binary
table extensions; their contents are listed in Table 4. The
file’s primary header-data unit (HDU 1) is copied directly
from the raw data file. (For histogram data, the SIA ta-
ble is discarded.) Various keywords are populated by the
initialization routine (§ 4.2) and by subsequent pipeline
modules. The first binary-table extension (HDU 2) con-
tains the photon events themselves. For time-tag data,
the TIME, XRAW, YRAW, and PHA arrays are copied
from the raw data file, and the WEIGHT array is ini-
tialized to 1.0. For histogram data, each image pixel
is mapped back to its coordinates on the full detector,
which are recorded in the XRAW and YRAW arrays.
The WEIGHT array is initialized to the number of pho-
ton events in the pixel. Zero-valued pixels are ignored.
Histogram data are not “unbinned.” Each entry of the
TIME array is set to the midpoint of the exposure and
each entry of the PHA array to 20. (Both arrays are
subsequently modified.)
The first pipeline module (§ 4.3) corrects for various
detector effects; it scales the WEIGHT array to correct
for detector dead time and populates the XFARF and
YFARF arrays. (The flight alignment reference frame
represents the output of an ideal detector.) Each pho-
ton event is assigned to one of six aperture-channel com-
binations or to the background (Table 5) and a corre-
sponding code is written to the CHANNEL array (§ 4.5).
After corrections for mirror, grating, and spacecraft mo-
tions, the photon’s final coordinates are recorded in the
X and Y arrays. Though floating-point arrays, XFARF,
YFARF, X, and Y are written to the IDF as arrays of
8-bit integers using the FITS TZERO and TSCALE key-
words. This process effectively rounds each element of
XFARF and X to the nearest 0.25 of a detector pixel
and each element of YFARF and Y to the nearest 0.1 of
a detector pixel.
The screening routines (§ 4.4) use information from the
timeline table (described below) to identify photons that
violate pulse-height limits, limb-angle constraints, etc.
“Bad” photons are not deleted from the IDF, but merely
flagged. Flags are stored as single bits in an 8-bit byte.
We use two sets of flags, TIMEFLGS for time-dependent
and LOC FLGS for location-dependent effects (Table 6).
For each bit, a value of 0 indicates that the photon is
“good,” except for the day/night flag, for which 0 = night
and 1 = day. It is possible to modify these flags without
re-running the pipeline. For example, one could exclude
day-time photons or include data taken close to the earth
limb.
The LAMBDA array contains the heliocentric wave-
length assigned to each photon (§ 4.6), and the ERGCM2
array records its “energy density” in units of erg cm−2 (§
4.7). To convert an extracted spectrum to units of flux,
one must divide by the exposure time and the width of
an output spectral bin.
The second extension (HDU 3) is a list of good-time
intervals (GTIs). The initial values are copied from the
raw data file, but they are modified by the pipeline once
the various screening routines have been run. By con-
vention, the START value of each GTI corresponds to
the arrival time of the first photon in that interval. The
STOP value is one second later than the arrival time of
the last photon in that interval. The length of the GTI
is thus STOP−START.
The third extension (HDU 4) is called the timeline ta-
CalFUSE: The FUSE Calibration Pipeline 21
ble. It contains status flags and spacecraft and detector
parameters used by the pipeline. An entry in the time-
line table is created for each second of the exposure. For
time-tag data, the first entry corresponds to the time of
the first photon event, and the final entry to the time of
the final photon event plus one second. (Should an expo-
sure’s photon-arrival times purport to exceed 55 ks, we
create timeline entries only for each second in the good-
time intervals.) For histogram data, the first element of
the TIME array is set to zero and the final element to
EXPTIME+1 (where EXPTIME is the exposure dura-
tion computed by OPUS). Because we require that EXP-
TIME equal both Σ (STOP−START), summed over all
entries in the GTI table, and the number of good times
in the timeline table, we must flag the final second of
each GTI as bad. No photons are associated with the
STOP time of a GTI.
Only the day/night and OPUS flags of the STA-
TUS FLAGS array are populated when the IDF is cre-
ated; the other flags are set by the various screening
routines (§ 4.4). The elements of the TIME SUNSET,
TIME SUNRISE, LIMB ANGLE, LONGITUDE, LAT-
ITUDE, and ORBITAL VEL arrays are computed from
the orbital elements in the FUSE.TLE file. The
HIGH VOLTAGE array is populated with values from
the housekeeping file. The LIF CNT RATE and
SIC CNT RATE arrays are initially populated with val-
ues derived from the LiF and SiC counter arrays in the
housekeeping file. For time-tag data, these arrays are
eventually updated with the actual count rates within
the target aperture, excluding regions contaminated by
airglow. The FEC CNT RATE and AIC CNT RATE,
described in § 4.3.2, are also derived from counter ar-
rays in the housekeeping file. For time-tag data, the
BKGD CNT RATE array is populated by the burst-
rejection routine (§ 4.4.5) and represents the count rate
in pre-defined background regions of the detector, ex-
cluding airglow features. The array is not populated for
histogram data. The YCENT LIF and YCENT SIC ar-
rays trace the centroid of the target spectra with time
before motion corrections are applied. These two arrays
are not used by the pipeline.
Raw time-tag files (§ A-1) employ the standard FITS
binary table format, listing TIME, X, Y, PHA for each
photon event in turn. The intermediate data files have a
slightly different format, listing all of the photon arrival
times, then the X coordinates, then the Y coordinates.
Formally, the table has only one row, and each element of
the table is an array. (To use the STSDAS terminology,
IDFs are written as 3-D tables.) The MDRFITS func-
tion from the IDL Astronomy User’s Library (Landsman
1993) can read both file formats; some older FITS read-
ers cannot. Note that, because HDUs 2 and 4 of the
IDFs contain floating-point arrays stored as shorts (using
the TZERO and TSCALE keywords), calls to MRDFITS
must include the keyword parameter FSCALE.
A-4. Bad-Pixel Maps (BPM Files)
The BPM files (suffix “bpm.fit”; § 4.8) consist of a
single binary table extension. Its format is similar to
that of the IDF, but it contains only five columns: X, Y,
CHANNEL, WEIGHT, and LAMBDA. The WEIGHT
column, whose values range from 0 to 1, represents the
fraction of the exposure that each pixel was affected by
a dead spot. The BPM files are not archived, but can
be generated from the IDF and jitter file using pipeline
software available from MAST.
A-5. Extracted Spectral Files
Extracted spectra (suffix “fcal.fit”; § 4.9) are stored
in a single binary table extension. Its contents are pre-
sented in Table 7. Note that the spectra are binned in
wavelength. The bin size can be set by the user, but the
default is 0.013 Å, which corresponds to about 2 detec-
tor pixels or about one-fourth of a spectral resolution el-
ement. The WAVE array records the central wavelength
of each spectral bin. For time-tag data, the COUNTS
array represents the total of all (raw) photon events as-
signed to the target aperture. For histogram data, the
COUNTS array is simply the WEIGHTS array divided
by the mean dead-time correction for the exposure. If op-
timal extraction is performed, the values of the FLUX,
ERROR, WEIGHTS, and BKGD arrays are determined
by that algorithm. As a result, the ratio of WEIGHTS
to COUNTS is constant only for histogram data. The
QUALITY array records the percentage of the extrac-
tion window containing valid data. It is 100 if no bad
pixels fell within the wavelength bin, 0 if the entire bin
was lost to bad pixels.
REFERENCES
Bohlin, R. C. 1996, AJ, 111, 1743
Bohlin, R. C., Colina, L., and Finley, D. S. 1995, AJ, 110, 1316
Feldman, P. D., Sahnow, D. J., Kruk, J. W., Murphy, E. M., and
Moos, H. W. 2001, J. Geophys. Res., 106, 8119
Hanisch, R. J., Farris, A., Greisen, E. W., Pence, W. D.,
Schlesinger, B. M., Teuben, P. J., Thompson, R. W., and
Warnock, A. 2001, A&A, 376, 359
Holberg, J. B., Barstow, M. A., and Sion, E. M. 1998, ApJS, 119,
Horne, K. 1986, PASP, 98, 609
Hubeny, I. and Lanz, T. 1995, ApJ, 439, 875
Kruk, J. W., Brown, T. M., Davidsen, A. F., Espey, B. R.,
Finley, D. S., and Kriss, G. A. 1999, ApJS, 122, 299
Landsman, W. B. 1993, in ASP Conf. Ser. 52: Astronomical Data
Analysis Software and Systems II, ed. R. J. Hanisch, R. J. V.
Brissenden, and J. Barnes (San Francisco: ASP), p. 246.
Lemoine, M. et al. 2002, ApJS, 140, 67
Massa, D. and Fitzpatrick, E. L. 2000, ApJS, 126, 517
McCandliss, S. R., France, K., Lupu, R. E., Burgh, E. B.,
Sembach, K., Kruk, J., Andersson, B-G, and Feldman, P. D.
2007, ApJ, in press (astro-ph/0701439)
Moos, H. W. et al. 2000, ApJ, 538, L1
Pence, W. 1999, in ASP Conf. Ser. 172: Astronomical Data
Analysis Software and Systems VIII, ed. D. M. Mehringer,
R. L. Plante, and D. A. Roberts (San Francisco: ASP), p. 487
Rose, J. F., Heller-Boyer, C., Rose, M. A., Swam, M., Miller, W.,
Kriss, G. A., and Oegerle, W. R. 1998, in Proc. SPIE 3349,
Observatory Operations to Optimize Scientific Return, p. 410
Sahnow, D. J. et al. 2000a, ApJ, 538, L7
Sahnow, D. J., Gummin, M. A., Gaines, G. A., Fullerton, A. W.,
Kaiser, M. E., and Siegmund, O. H. 2000b, in Proc. SPIE 4139,
Instrumentation for UV/EUV Astronomy and Solar Missions,
ed. S. Fineschi, C. M. Korendyke, O. H. Siegmund, and B. E.
Woodgate, p. 149
Siegmund, O. H. et al. 1997, in Proc. SPIE 3114, EUV, X-Ray,
and Gamma-Ray Instrumentation for Astronomy VIII, ed.
O. H. Siegmund and M. A. Gummin, p. 283
Tremsin, A. S., Siegmund, O. H., Gummin, M. A., Jelinsky,
P. N., and Stock, J. M. 1999, Appl. Opt., 38, 2240
http://arxiv.org/abs/astro-ph/0701439
22 Dixon et al.
Al+LiF Coated 
Mirror #2
Focal Plane 
Assemblies (4)
Detectors (2) 
Al+LiF Coated 
Mirror #1
SiC Coated 
Mirror #2
SiC Coated 
Mirror #1
Rowland Circles
Al+LiF Coated
Grating #2
Al+LiF Coated
Grating #1
SiC Coated
Grating #2
Fig. 1.— Schematic of the FUSE instrument optical system. The telescope focal lengths are 2245 mm, and the Rowland circle diameters
are 1652 mm. (Figure from Moos et al. 2000.)
CalFUSE: The FUSE Calibration Pipeline 23
-150 -100 -50 0 50 100 150
X Coordinate (arcsec)
Fig. 2.— The FUSE apertures projected onto the sky. In the FPA coordinate system, the LWRS, HIRS, and MDRS apertures are centered
at Y = −118.′′07, −10.′′27, and +90.′′18, respectively. The reference point (RFPT) at X = +55.′′18 is not an aperture; when a target is
placed at this location, the three apertures sample the background sky. With north on top and east on the left, this diagram corresponds
to an aperture position angle of 0◦. Positive aperture position angles correspond to a counter-clockwise rotation of the spacecraft about
the target aperture. This diagram represents only a portion of the FPA; its active area is 19′×19′.
24 Dixon et al.
0 5.0•103 1.0•104 1.5•104
2.0•103 4.0•103 6.0•103 8.0•103 1.0•104 1.2•104 1.4•1040
1000 1020 1040 1060 1080
1000 1020 1040 1060 10801080 1060 1040 1020
1080 1060 1040 1020
Wavelength (Å)
Fig. 3.— Image of detector segment 1A during a bright-earth observation. All lines are geocoronal. Note the strong Lyman β (1026 Å)
feature in each spectrum. The data have been fully corrected for detector and other distortions. Extended-source extraction windows for all
three apertures in both the LiF and SiC channels are marked; point-source extraction windows are somewhat narrower in Y. Instrumental
astigmatism is responsible for the bow-tie shape of each spectrum. The region shown corresponds to detector pixels 900 to 15,300 in X and
0 to 915 in Y.
Fig. 4.— Image of detector 1A in raw X and Y coordinates showing geometric distortion. The image shows only a portion of the detector.
It was constructed from 3 separate exposures with stars in the HIRS, MDRS, and LWRS apertures of the LiF1A detector.
CalFUSE: The FUSE Calibration Pipeline 25
Fig. 5.— Segments of detector 1A images showing filamentary (top) and checkerboard (bottom) bursts. Checkerboard bursts typically
fill the detector, save for the region around the LiF Lyman β lines on detector 1A.
Fig. 6.— Segment of LiF1A spectrum before (bottom) and after (top) astigmatism correction. Note the reduction of curvature in the
absorption features. A detector dead spot is present on the left side of the figure.
Fig. 7.— Night-time scattered-light image for detector 1A. Note the vertical scattered-light stripe to the right of the image center.
26 Dixon et al.
0 5000 10000 15000
X Coordinate (Pixels)
Fig. 8.— Geometric distortion in the X coordinate of the LiF1A LWRS channel. Data represent the difference between the measured
locations of H2 lines in the spectrum of GCRV 12336 and those predicted by a theoretical dispersion relation. The solid line is a spline fit
to the residuals.
0 10 20 30 40 50
Observation Date
2001/09
2002/08
2002/10
2003/12
2004/07
2006/12
Fig. 9.— Measured heliocentric velocity of the interstellar O I λ1039.23 absorption feature in each of 47 exposures of the white dwarf
KPD 0005+5106 through the high-resolution (HIRS) aperture. Holberg et al. 1998 report a heliocentric velocity of −7.50 ± 0.76 km s−1
for the interstellar features along this line of sight.
CalFUSE: The FUSE Calibration Pipeline 27
2000 2002 2004 2006
Date (Year)
LiF1A
SiC1A
980 1000 1020 1040 1060 1080 1100
Wavelength (Å)
LiF1A
SiC1A
Fig. 10.— FUSE sensitivity as a function of time. Upper panel: Effective area of the LiF1A and SiC1A channels, averaged over the
wavelength region 1030–1040 Å. The gap between 2004 October and 2006 May represents the period after the loss of the third reaction
wheel, when few calibration targets were observed. Lower panel: Effective-area curves for the LiF1A and SiC1A channels, dated 1999 and
2006. (For both channels, the 1999 curve has the higher effective area.)
28 Dixon et al.
1100 1120 1140 1160 1180
Wavelength (Å)
Fig. 11.— Point-source spectra showing the effects of the worm. Spectra A and B, obtained with the LiF1B channel, show deep depressions
near 1145 and 1160 Å, respectively. The wavelength of maximum attenuation varies with the Y position of the target within the aperture.
Spectrum C, obtained with the LiF2A channel, is unattenuated.
0 2000 4000 6000
Fig. 12.— Detector images showing the effects of the worm. In these negative images, worms appear as bright stripes parallel to the
dispersion axis. The data shown correspond to spectra A and B in Fig. 11 and span wavelengths between 1134 and 1187 Å.
1044.0 1044.5 1045.0 1045.5 1046.0
Wavelength (Å)
Fig. 13.— Moiré pattern in the LiF2B spectrum of the star HD 209339, obtained in histogram mode. The associated error array is
overplotted. The moiré ripples are strongest on this detector segment, but are also seen on segments 1A and 1B.
CalFUSE: The FUSE Calibration Pipeline 29
TABLE 3
Format of Raw Histogram Files
Image Sizea
HDU Contents (binned pixels)
1b SIA Tablec 8× 64
2 SiC Spectral Image (12–20) × 16384
3 LiF Spectral Image (12–20) × 16384
4 Left Stim Pulse 2 × 2048
5 Right Stim Pulse 2 × 2048
Note. — While this table describes the format of a typical
raw histogram file, any number of HDUs are allowed.
a Quoted image sizes assume the standard histogram binning: by
8 pixels in Y, unbinned in X. Actual binning factors are given in
the primary file header.
b Header keywords of HDU 1 contain exposure-specific informa-
tion.
c The SIA table describes which regions of the detector are in-
cluded in the file.
TABLE 4
Format of Intermediate Data Files
Array Name Format Description
Primary Header-Data Unit (HDU 1)
Header only. Keywords contain exposure-specific information.
HDU 2: Photon Event List
TIME FLOAT Photon arrival time (seconds)
XRAW SHORT Raw X coordinate (0–16383)
YRAW SHORT Raw Y coordinate (0–1023)
PHA BYTE Pulse height (0–31)
WEIGHT FLOAT Photons per binned pixel for HIST data,
initially 1.0 for TTAG data
XFARF FLOAT X coordinate in geometrically-corrected frame
YFARF FLOAT Y coordinate in geometrically-corrected frame
X FLOAT X coordinate after motion corrections
Y FLOAT Y coordinate after motion corrections
CHANNEL BYTE Aperture+channel ID for the photon (Table 5)
TIMEFLGS BYTE Time flags (Table 6)
LOC FLGS BYTE Location flags (Table 6)
LAMBDA FLOAT Wavelength of photon (Å)
ERGCM2 FLOAT Energy density of photon (erg cm−2)
HDU 3: Good-Time Intervals
START DOUBLE GTI start time (seconds)
STOP DOUBLE GTI stop time (seconds)
HDU 4: Timeline Table
TIME FLOAT Seconds from exposure start time
STATUS FLAGS BYTE Status flags
TIME SUNRISE SHORT Seconds since sunrise
TIME SUNSET SHORT Seconds since sunset
LIMB ANGLE FLOAT Limb angle (degrees)
LONGITUDE FLOAT Spacecraft longitude (degrees)
LATITUDE FLOAT Spacecraft latitude (degrees)
ORBITAL VEL FLOAT Component of spacecraft velocity
in direction of target (km/s)
HIGH VOLTAGE SHORT Detector high voltage (unitless)
LIF CNT RATE SHORT LiF count rate (counts/s)
SIC CNT RATE SHORT SiC count rate (counts/s)
FEC CNT RATE FLOAT FEC count rate (counts/s)
AIC CNT RATE FLOAT AIC count rate (counts/s)
BKGD CNT RATE SHORT Background count rate (counts/s)
YCENT LIF FLOAT Y centroid of LiF target spectrum (pixels)
YCENT SIC FLOAT Y centroid of SiC target spectrum (pixels)
Note. — Times are relative to the exposure start time, stored in the header
keyword EXPSTART. To conserve memory, floating-point values are stored as shorts
(using the FITS TZERO and TSCALE keywords) except for TIME, WEIGHT,
LAMBDA and ERGCM2, which remain floats.
30 Dixon et al.
TABLE 5
Aperture Codes for IDF
CHANNEL Array
Aperture LiF SiC
HIRS 1 5
MDRS 2 6
LWRS 3 7
Not in an aperture 0
TABLE 6
Bit Codes for IDF Time and
Location Flags
Bit Value
Time Flags
8 User-defined bad-time interval
7 Jitter (target out of aperture)
6 Not in an OPUS-defined GTI or
Photon arrival time unknown
5 Burst
4 High voltage reduced
3 SAA
2 Limb angle
1 Day/Night flag (N = 0, D = 1)
Location Flags
8 Not used
7 Fill data (histogram mode only)
6 Photon in bad-pixel region
5 Photon pulse height out of range
4 Right stim pulse
3 Left stim pulse
2 Airglow feature
1 Not in detector active area
Note. — Flags are listed in order from
most- to least-significant bit.
TABLE 7
Format of Extracted Spectral Files
Array Name Format Description
Primary Header-Data Unit (HDU 1)
Header only. Keywords contain exposure-specific information.
HDU 2: Extracted Spectrum
WAVE FLOAT Wavelength (Å)
FLUX FLOAT Flux (erg cm−2 s−1 Å−1)
ERROR FLOAT Gaussian error (erg cm−2 s−1 Å−1)
COUNTS INT Raw counts in extraction window
WEIGHTS FLOAT Raw counts corrected for dead time
BKGD FLOAT Estimated background in extraction window (counts)
QUALITY SHORT Percentage of window used for extraction (0–100)
