**A Low-Redshift Supernova Survey Using the ARC 2.5m Telescope**

S. Asztalos^1^, B. Bassett^2,3^, A. Becker^4^, R. Blandford^5^, F.
Cattaneo^6^, D. Cinabro^7^, C. Clarkson^3^, D. DePoy^8^, B. Dilday^6^,
M. Doi^9^, J. Frieman^6^, P. Garnavich^10^, S. Hawley^4^, C. Hogan^4^,
J. Holtzman^11^, S. Jha^5,12^, J. Johnson^8^, S. Kahn^5^, R. Kessler^6^,
A. Khoklov^6^, D. Lamb^6^, H. Lampeitl^13^, K. Maeda^9^, R. Nichol^14^,
K. Nomoto^9^, S. Olivier^1^, J. Prieto^8^, M. Richmond^15^, A.
Riess^12,16^, R. Romani^5^, M. Sako^5,17^, D. Schneider^18^, N.
Suntzeff^19^, M. Tanaka^9^, N. Tominaga^9^, K. van der Heyden^2^, N.
Yasuda^9^, D. York^6^, L. Wang^1,19^

^1^*Lawrence Livermore National Laboratory* ^2^*South African
Astronomical Observatory*

^3^*University of Cape Town* ^4^*University of Washington* ^5^*Stanford
University*

^6^*University of Chicago* ^7^*Wayne State University* ^8^*Ohio State
University*

^9^*University of Tokyo* ^10^*University of Notre Dame* ^11^*New Mexico
State University*

*^12^Rutgers University* ^13^*Space Telescope Science Institute
^14^University of Portsmouth*

*^15^Rochester Institute of Technology ^16^Johns Hopkins University
^17^University of Pennsylvania ^18^Pennsylvania State University
^19^Texas A&M University*

# 

# Abstract

We propose to devote the ARC 2.5m telescope and its imager to a new
supernova survey during the first ten months of operations after the end
of SDSS II (Sept. 2008 through June 2009). In combination with and
building upon the success of the SDSS II Supernova Survey, the ARC
Supernova Survey (ARCSS) will discover and measure densely sampled,
well-calibrated, multi-band optical light curves for \~250 low-redshift
type Ia supernovae, quadrupling the SDSS II sample in the range
0.05\<z\<0.15. This sample will be an *order of magnitude* larger than
currently available low-redshift SN samples of comparable data quality.
With less stringent quality cuts, the ARCSS sample will total \~500
light curves. The ARCSS survey efficiency will be well above 90%,
resulting in an unbiased sample. The survey will also probe other
supernova types, and its large volume will enable study of peculiar and
relatively rare supernovae, including peculiar SNe Ia and Ib/c. To
achieve such a high SN discovery rate, ARCSS will scan at three times
sidereal rate. We will carry out tests to ensure that we can operate in
this mode and obtain precision photometry using the associated binned
readout; if that proves infeasible, scanning at sidereal rate will
produce a sample five times larger than the comparable, current
low-redshift sample. Spectroscopic follow-up observations to determine
supernova types, obtain precise redshifts, and measure detailed
spectroscopic features at several epochs per supernova will be provided
by a number of telescopes around the world. The proposed ARCSS sample
will result in significantly improved anchoring of the Hubble diagram
and understanding of supernova luminosity variations and K-corrections;
combining this information with ongoing and future high-redshift surveys
will lead to stronger and more robust constraints on the nature of the
dark energy.

# 1. Introduction: Motivation and Survey Overview

The use of supernovae to measure cosmological distances led to the
discovery of the accelerating Universe in 1998 (Riess, et al. 1998,
Perlmutter, et al. 1999). Since then, the field has expanded rapidly,
and the evidence for cosmic acceleration from supernovae, the cosmic
microwave background anisotropy, and the large-scale clustering of
galaxies has solidified (e.g., Riess, et al. 2004, Tegmark, et al.
2004). Several national reports, including *From Quarks to the Cosmos*
and *the Physics of the Universe*, have identified the nature of the
dark energy as one of the most profound questions about the Universe.
While several promising techniques for probing dark energy are being
developed, supernovae remain \`\`at present the most powerful and best
proven technique for studying dark energy", according to the recent
*Report of the Dark Energy Task Force*.

These considerations motivated the SDSS II Supernova program, which
comprises three 3-month observing periods in Fall 2005, 2006, and 2007.
The SDSS II Supernova Survey is designed to fill in the \`redshift
desert' in the SN Ia Hubble diagram at redshifts 0.05 \< z \< 0.35 with
a large, well-calibrated sample of supernova light curves. This sample
will be used to probe dark energy and explore systematics of the SN Ia
population that impact distance estimates. The first full season of the
survey in 2005 proved extremely successful, with discovery and
measurement of about 130 spectroscopically confirmed type Ia supernovae
with median redshift of 0.2. Spectroscopic confirmation and redshifts
were provided by a coordinated follow-up program using a number of
telescopes, including the ARC 3.5m, MDM 2.4m, WHT 4.2m, HET 9.2m, Subaru
8.2m, and Keck 10m. Additional optical photometry was supplied by the
UH88in, NMSU 1m, MDM 2.4m, ARC 3.5m, and VATT, while optical and
near-infrared follow-up was carried out by the Carnegie Supernova
Project on a subsample. Photometry and spectroscopy of several of the
2005 objects were also carried out by the CfA group at Mt. Hopkins upon
their public release. The Fall 2005 SDSS data are now being analyzed,
with first results expected later in 2006. (For further information, see
<http://sdssdp47.fnal.gov/sdsssn_data/sdsssn.html> and Sako, et al.
2004).

The increasing importance of supernovae as \`standardizable' candles for
cosmology has motivated a number of other on-going surveys at high
redshift, e.g., ESSENCE, SNLS, and several programs using HST, as well
as ambitious proposals for future high-redshift supernova surveys from
the ground, e.g., PanSTARRS, DES, LSST, and from space, e.g., the Joint
Dark Energy Mission (JDEM) supernova proposals SNAP, DESTINY, and JEDI.
Since supernovae provide relative distance estimates, these
high-redshift surveys must rely on a low-redshift supernova sample to
anchor the Hubble diagram. Well-sampled, low-redshift supernova light
curves are also needed to train the light-curve fitting algorithms used
to infer high-redshift SN distances and to span the range of SN Ia
sub-types encountered at high redshift (including variations in SN
luminosity, reddening, metallicity, and galaxy host type), so that
high-redshift \`apples' (i.e., Ia sub-types) can be directly compared to
low-redshift \`apples'. Moreover, well-calibrated, multi-epoch,
low-redshift supernova spectra are needed to infer the
redshift-dependent K-corrections that enter into SN distance estimates.

There has been a growing recognition that the current and currently
forecast low-redshift supernova samples are not of the requisite size
and data quality to fully support the goals of supernova cosmology: the
cosmological power of on-going, high-redshift SN surveys is already
limited statistically and systematically by the small size,
heterogeneous nature, and often poor light-curve sampling of the
available low-redshift SNe. To address these concerns, the *Report of
the Dark Energy Task Force* recently called for \`\`detailed
spectroscopic and photometric observations of about 500 nearby
supernovae to study the variety of peak explosion magnitudes and any
associated observational signatures of effects of evolution,
metallicity, or reddening". The ARCSS is designed to meet these goals.

A number of low-redshift SN surveys are underway: the Katzman Automatic
Imaging Telescope (KAIT) and the Nearby Supernova Factory (SNF) are
carrying out searches and follow-up observations, while the Carnegie
Supernova Project (CSP) and the CfA Supernova Group (CfA) are carrying
out follow-up programs; SkyMapper is a 1.35m telescope under
construction at Siding Spring Observatory. A number of features
differentiate ARCSS from past and on-going low-z surveys:

1.  The on-going surveys focus on very low redshifts, z\<0.08; recent
    work (Hui & Greene 2005, Cooray & Caldwell 2006) has shown that this
    regime is more affected by correlated peculiar velocities than
    previously recognized (see Sec. 2.1). ARCSS will target a higher but
    still low-redshift interval, 0.05\<z\<0.15, that is intrinsically
    less affected by this source of systematic error in distance
    estimates.

2.  With its high cadence and moderate depth, ARCSS will measure
    well-sampled light curves and in particular have high efficiency for
    early-time (pre-maximum light) photometry. Early-time photometry
    increases the utility of the objects as a training sample because
    the time of maximum is better constrained.

3.  ARCSS will search with very high efficiency (\>90%), even for
    peculiar and highly extincted type Ia supernovae, and with no
    explicit bias with respect to host galaxy brightness.

4.  ARCSS will combine precision photometry with multi-epoch
    spectroscopy to improve K-corrections; the current set of spectra
    used for K-corrections is very heterogeneous, and contains only
    \~200 spectra for all phases of the light curve.

The SDSS II Supernova Survey has demonstrated that the ARC 2.5m is
capable of delivering a photometric supernova sample of the requisite
size and high data quality in a relatively modest amount of observing
time. The ARCSS will build upon the extensive operational experience and
data processing expertise that our team has built up during the
preparation for, operation of, and analysis of the SDSS II Supernova
survey. The dedicated compute cluster we have in place at APO reliably
and routinely processes a full night of SDSS imaging through photometric
reduction, frame subtraction, and automated target screening in under 24
hours, and we expect that performance to extend to the slightly lower
ARCSS pixel-data rate. Essentially the entire team that has been
actively involved in SDSS II photometric data processing, candidate
target selection, follow-up spectroscopy, and data analysis has
expressed interest in collaborating on ARCSS, and we view this
collective experience as a tremendous asset to the viability of the
project.

Using Monte Carlo simulations, we have considered and compared several
survey strategies. We find that increasing the drift scan rate from
sidereal to approximately three times sidereal and maintaining the
two-day cadence for each sky region is nearly optimal for maximizing the
number of well-sampled SN Ia light curves in the redshift regime
z\<0.15. Fig. 1 shows that we expect to measure approximately 175
high-quality SN Ia light-curves at z\<0.15 in this survey mode over a
9-month period, quadrupling the sample that SDSS II will accumulate in
this redshift range; for this exercise, the criterion for a
\`high-quality' light curve is that it comprise at least 7 well-measured
(i.e., with signal to noise above 5) epochs and include epochs both
before and after maximum light. This Monte Carlo prediction has been
\`normalized' to the Fall 2005 survey results, which yielded 20 such
\`high-quality' light curves in this redshift range over the three-month
season and 40 spectroscopically confirmed Ia's in this range overall. As
a result, we expect the combined SDSS II+ARCSS sample to comprise about
250 high-quality Ia light-curves at z\<0.15 and approximately 500 total
confirmed Ia's in that range (most of the additional 250 with quite
useful light curves as well).

![](media/image1.png){width="5.116666666666666in"
height="4.334722222222222in"}

> Figure 1. Monte Carlo simulated redshift distribution of SNe Ia for
> the SDSS II SN Survey (green dashed) and for ARCSS (solid red). In
> each case, the numbers are for a total of 9 months of observations and
> have been scaled to agree with the 20 high-quality light curves
> observed at z\<0.15 in the Fall 2005 run. While the total number of
> supernovae is similar for SDSS II and ARCSS, the number at z\<0.15 is
> \~3 times greater for ARCSS.

Scanning at faster than sidereal rate requires that the ARC 2.5m camera
be read out with 2x2 pixel binning. The simulation results in Fig. 1
take into account the increased noise due to both the lower effective
exposure time and the larger effective pixel area associated with faster
scanning. Fig. 2 shows two supernova light curves from the 2005 SDSS II
sample, at the high and low ends of the ARCSS redshift range, with
photometric errors artificially scaled up to reflect the faster, binned
scans, demonstrating that the increased statistical errors will not
compromise the quality of the light curves. The *systematic* effects of
larger pixels on SN photometry will require further study; the results
of preliminary studies, which indicate that operating in binned mode
should be able to meet the science requirements, are discussed below in
Sec. 5.4.

While the 2.5m camera has been used in binned mode (e.g., the 4x4 Apache
wheel scans), carrying out a new survey in binned mode will require a
well-organized software development and testing effort. We discuss the
development and operational issues associated with binned readout in
Sec. 5; an end-to-end test run in faster, binned

![](media/image2.jpeg){width="2.839583333333333in"
height="3.433333333333333in"}

![](media/image3.png){width="2.9368055555555554in"
height="3.404166666666667in"}

> Figure 2: Light-curves and preliminary fits for two SDSS II type Ia
> supernovae roughly spanning the ARCSS redshift range (z-band not
> shown): (a) SN2005fm, at redshift z=0.1528 (internal SDSS designation
> SN2031); (B) SN2005hc, at z=0.0459 (internal SN5944). Errors have been
> artificially increased to reflect faster, binned scans. (The SN5944
> light curve is better sampled due to weather variations, and because
> it lies in the overlap region between SDSS strips 82N and 82S.)

mode (which we propose to carry out in Fall 2006) will be a crucial
feasibility test and is an integral part of the development plan. If
this test or others reveal insurmountable barriers to meeting the
science goals in binned mode, our alternative strategy would be for
ARCSS to scan at sidereal rate and to focus the follow-up resources on
the z\<0.15 portion of the sample. This would reduce substantially the
needed development effort and modestly reduce the cost for the project;
over a ten-month survey period, the alternative strategy would more than
double the low-redshift sample from SDSS II.

An important ingredient of this proposal is the aim of obtaining
multi-epoch spectroscopy, ideally with reasonable spectrophotometric
accuracy for the bulk of the \`high quality' sample at z\<0.15. Our goal
is 10% to 20% accuracy from the spectrographs, refined to a few percent
when warped to match the 2.5m photometry. Much (but not all) of this
follow-up can be carried out on telescopes with relatively modest
apertures (2 to 4 meters). As discussed in Secs. 2.4 and 2.7, this rich
spectroscopic sample will enable us to obtain improved K-corrections, to
study variations in spectroscopic features and how they correlate (or
not) with photometric properties, and it will provide important
diagnostics for theoretical models of SN Ia explosions. We discuss the
needed and expected spectroscopic resources in Sec. 4. Fig. 3 shows
low-redshift, high signal-to-noise SDSS II spectra taken with the WHT
4.2m and the MDM 2.4m telescopes for the two supernovae with light
curves shown in Fig. 2.

![](media/image4.jpeg){width="3.375in" height="3.2125in"}

![](media/image5.jpeg){width="3.2465277777777777in"
height="3.2444444444444445in"}

> Figure 3: Spectra for same SDSS II supernovae of Fig. 2, showing
> characteristic type Ia features and roughly spanning the ARCSS
> redshift range: (a) SN2005fm, taken with the WHT 4.2m telescope \~2
> days after peak; (b) SN2005hc, taken at peak with the MDM 2.4m
> telescope.

**2. Science Goals:**

**2.1 Anchoring the Hubble Diagram**

The luminosity distance-redshift relation shows strong dependence on
cosmological parameters, such as the amount and equation of state of
dark energy, at moderate to high redshifts. Supernovae probe this
relation via relative distance estimates, since the peak absolute
magnitude of SNe Ia is not precisely determined. Thus, to probe
cosmology requires high-redshift SN in the matter-dominated era of the
expansion, as well as low-redshift SN in the era where dark energy is
dominant.

One of the primary goals of the ARCSS will be to significantly improve
the low-redshift \"anchor\" to the Hubble diagram, which will directly
improve the cosmological precision achievable from on-going and future
supernova surveys. As one measure of the paucity of the currently
available low-redshift samples, the analysis of the first year of data
from the Supernova Legacy Survey (SNLS, Astier et al. 2005) was based on
73 SNe at z\>0.25 from SNLS but only 44 public, nearby \`anchor'
supernovae (z\<0.15) that satisfied the rather weak light-curve quality
cuts they imposed (which included: z\>0.015 to limit peculiar velocity
effects \[though see below\], and the requirement that the first
photometric point was no more than 5 days *after* maximum light). As
another measure, the public sample of 89 low-redshift SNe Ia used by
Prieto, et al. (2006) to calibrate a new SN Ia distance estimator
included only \~25 that were simultaneously observed in more than two
passbands, reasonably densely sampled (i.e., more than 2 or 3 epochs of
photometry), and with first photometric point no later than maximum
light. ARCSS+SDSS II will provide a low-redshift sample an *order of
magnitude larger* (\~250) that satisfies even more stringent light-curve
quality cuts: 5 passbands, densely sampled (at least 7 photometry epochs
above signal-to-noise of 5), uniformly selected, and with first epoch
before maximum light.

Fig. 4 shows the importance of having a low-redshift anchor sample of
such size: with intermediate-to-high redshift SNe samples (from SNLS and
ESSENCE) approaching \~500, a sample of \~250 low-redshift supernovae is
needed to exploit their potential to achieve precise constraints on the
dark energy equation of state. The same conclusions extend to future SN
surveys such as those proposed for JDEM.

The scientific motivation for better anchoring of the Hubble diagram is
not driven by theoretical considerations alone: the current low-redshift
samples already show evidence for *systematic* effects that limit
precision cosmology from supernovae. Jha, Riess, & Kirshner (2006) have
found stronger evidence for a \`Hubble Bubble' at z=0.025, with the
Hubble parameter (i.e., the intercept of the low-z SN magnitude/redshift
relationship) about 6% higher nearby than beyond that distance. This may
be evidence of a large void at that scale, or it may indicate a
systematic effect in the nearby SN samples. The resolution of this issue
will have a major impact on cosmology: if one exchanges the lower-z
sample (z\<0.025) for the z\>0.025 sample as the anchor, the inferred
value of *w* from the *same* current high-redshift supernovae (e.g.,
ESSENCE or SNLS) changes by as much as 0.4, several times larger than
the statistical precision shown in Fig. 4. In order to help resolve this
puzzle, a new, well-controlled, low-redshift sample at somewhat larger
distances, such as ARCSS will provide, will be ideal.

![](media/image6.png){width="4.170833333333333in"
height="4.170833333333333in"}

> Figure 4: Forecast 68% CL statistical errors on dark energy equation
> of state parameter *w* from high-z (0.2\<z\<0.8) supernova surveys of
> size 70 (e.g., first-year SNLS) and 500 (expected ESSENCE+SNLS in
> \~2007-8) vs. number of low-z (z\<0.15) anchor SNe: (a) marginalizing
> over Ω~m~; (b) same, but with prior from SDSS LRG baryon acoustic
> oscillations (Eisenstein et al. 2005). In both cases, a flat Universe
> and constant *w* are assumed for illustration. Low-z sample assumes
> pure Hubble distribution; high-z sample assumes the measured SNLS
> redshift distribution from Astier et al. 2005.

The conventional wisdom on the optimal redshift range for the anchor
sample has recently changed. Hui and Greene (2005) and Cooray and
Caldwell (2006) showed that *correlated* large-scale peculiar
velocities, neglected in previous studies, lead to larger systematic
errors in the low-redshift Hubble diagram than previously realized. When
these effects are included, Fig. 5 shows that the optimal redshift range
for the anchor sample falls in a broad range starting above z \~ 0.06
and extending beyond z=0.12, depending on the area covered by the
survey. As Fig. 1 shows, the redshift distribution for ARCSS is very
well matched to this range. By contrast, of the 44 nearby supernovae
used in the first-year SNLS analysis, only 10 were at redshift z\>0.05;
the sample of 89 nearby supernovae used for calibration by Prieto, et
al. (2006) included only 12 at z\>0.05. The on-going low-redshift
supernova searches are primarily targeting very-low redshift, e.g.,
z\<0.04 for KAIT and z\<0.08 for the Supernova Factory. The ARCSS will
complement both of these lower-z surveys. Fig. 5 shows that a large
survey area mitigates the systematic impact of large-scale flows; ARCSS
will cover a very large area (\~1600 sq. deg.) and will include widely
separated areas in the north and south (see Fig. 7).

![](media/image7.wmf){width="4.329133858267716in"
height="3.651968503937008in"}

> Figure 5: Forecast rms error on dark energy equation of state from
> SNAP vs. redshift of low-z anchor sample of 300 SNe covering 1000 or
> 20,000 sq. deg. (from Hui & Greene 2006). Top (bottom) plot assumes
> residual dispersion of relative supernova magnitudes of 15(10)%.
> *w*~pivot~ is dark energy equation of state at redshift where it is
> best determined.

**2.2 Systematic Studies for Supernova Cosmology**

Future supernova surveys such as those proposed for JDEM aim to achieve
exquisite precision in distance estimates, of order 1% distance error in
redshift bins of width 0.1 out to redshifts approaching z\~2, by
measuring thousands of SNe Ia. That level of precision would enable
determination of the present dark energy equation of state *w* at the
few percent level and of its time variation at the 20-30% level (in
combination with other measurements). To achieve that precision,
systematic errors must be controlled to the same level. Examples of
systematic errors that could compromise these goals include evolution of
the Ia population, variations or evolution of metallicity, and
variations or evolution of host galaxy reddening. To quantify such
systematic effects, it is necessary to assemble a high-quality,
homogeneously selected SN sample of sufficient size to enable breaking
into multiple subsamples to study correlation of SN distance estimates
with other observables, e.g., host galaxy properties. The ARCSS+SDSS II
sample will include \~250 high-quality (\~500 total) light-curves at
z\<0.15, plus another \~150-200 light-curves at z\<0.3 from SDSS II,
sufficient for division into subsamples for a variety of systematics
studies.

**2.3 Light Curve Templates**

Supernova cosmology rests in part on the empirical correlation between
light curve shape and peak luminosity (Phillips 1993); taking into
account this correlation through light curve fit techniques, SNe Ia
provide relative distance estimates with a dispersion of about 7%.
Another motivation for ARCSS is the clear need for a much larger sample
of low-redshift light curves with both multi-band observations and
photometric coverage well before, near, and after maximum light. Such a
sample will enable development and training of improved light curve fit
algorithms, e.g., iterations of or developments beyond such methods as
∆m~15~, MLCS, stretch, and CMAGIC (e.g., Phillips 1993, Riess, Press, &
Kirshner 1996, Jha 2002, Jha, Riess, & Kirshner 2006, Wang, et al. 2003,
Prieto, et al. 2006). As noted above, the currently available
low-redshift samples used for training light curve fitters contain very
few supernovae with photometry before maximum light; as a result,
pre-maximum-light photometry of intermediate to high-redshift supernovae
cannot be optimally used in determining SN distances. Since early-epoch
photometry helps constrain light curve shape, the distance estimates for
current high-redshift supernovae are limited in part by this drawback of
the low-z sample. The rolling nature, high cadence, and depth of the
ARCSS and SDSS II surveys enable us to obtain well-sampled light curves
starting well before maximum light, as Fig. 2 demonstrates.

**2.4 Supernova K-corrections and Spectroscopic Correlations**

Since light curve fits are based on low-redshift templates,
high-redshift light curve data are usually transformed into equivalent
rest-frame photometry before fitting. This transformation\-\--the
K-correction\-\--in principle requires knowledge of the supernova
spectrum at each photometric epoch. In practice, one assumes that the
high-z spectrum is similar to a typical or median spectrum for a
low-redshift supernova at the same epoch, and the latter are \`warped'
by some prescription to yield synthetic colors that agree with the
photometry of the high-z supernova. Clearly this prescription does not
account for variations in spectral features and their suggested
correlations with SN photometric properties (Benetti et al. 2005). One
goal of ARCSS will be to assemble a set of high signal-to-noise spectra
at several epochs for each supernova with a high-quality light curve,
enabling (a) detailed study of such features and correlations and (b)
construction of improved K-correction spectral templates.

One issue currently under study is the level of spectrophotometric
accuracy needed for constructing better K-correction templates and the
optimal strategy for achieving that accuracy. In addition to
wavelength-dependent slit losses, which can be limited to some degree by
use of wide slits and by observing at the parallactic angle, there will
be contamination from host galaxy light which can be subtracted by
additional exposures with either the slit displaced away from the
supernova or at a later epoch after the supernova has faded. In a
preliminary study of the SDSS II spectra taken during 2005, SDSS colors
were synthesized for each spectrum and compared with SDSS photometry
interpolated to the given spectroscopic epoch. The agreement was at the
few % level for objects brighter than *r*=18 but differed systematically
at fainter magnitudes, presumably due to slit losses and host galaxy
contamination; an exploratory effort to subtract the latter using
Principal Component Analysis is just starting. Fig. 6 shows an example
of this comparison for one of the low-redshift SDSS II supernovae with
multi-epoch spectroscopy. Since the ARCSS light curves will be densely
sampled and in multiple passbands, in principle they can be interpolated
to the spectroscopic epochs and used to correct (or warp) the overall
shapes of the spectral energy distributions. This is likely to be useful
in cases where the host galaxy contribution to the extracted supernova
spectrum can be subtracted or is very small, so that warping will
correct for differential slit losses.

![](media/image8.png){width="3.9402777777777778in"
height="3.9451388888888888in"}

> Figure 6. Comparison of interpolated (\`observed') SDSS colors with
> those synthesized from MDM spectra for the low-redshift (z=0.062) SDSS
> II supernova 2005gj.

**2.5 Peculiar Type Ia Supernovae**

Peculiar type Ia supernovae, which comprise perhaps 10% of the Ia
population, have different observed luminosities compared to normal type
Ia's and can potentially bias cosmological results if not properly
accounted for. If peculiar supernovae affect both low- and high-redshift
samples identically, any biases should cancel; in practice, however, the
selection of peculiar Ia's in these samples may differ. A good
understanding of the population, variability, and observational
signatures of peculiar type Ia's is likely to be necessary for precision
cosmology with supernovae. Since it covers a very large volume, ARCSS
will offer an unprecedented opportunity to study peculiar supernovae in
a statistically meaningful way.

Peculiar supernovae are often classified as 1991T-like (overluminous)
and 1991bg-like (underluminous). While at the extremes of observed peak
luminosity for Ia's, these supernovae do fit the empirical correlation
(Phillips 1993) between peak luminosity and light-curve decline rate;
they may simply represent extremes of the normal distribution of type Ia
supernovae, in which the main features of the light curve and spectra
follow from the amount of Ni^56^ produced in the supernova explosion. On
the other hand, the advent of larger SN samples is beginning to uncover
small numbers of peculiar supernovae, such as 2002cx (Li, et al. 2003,
Jha et al. ), that do not fit easily into this picture: this object may
be a new twist in the distribution of "normal" supernovae or it may be
the manifestation of a new phenomenon. SN2005hk, co-discovered by SDSS
II and subjected to intense follow-up observations, is another example
of this class (Jha, et al. 2006). SN2005gj (Prieto, et al. 2005,
Aldering, et al. 2006) is another example of a peculiar supernova for
which SDSS II obtained multiple follow-up spectra; it is a rare instance
of a type Ia with Hydrogen Balmer emission lines, similar to SN2002ic
(Hamuy, et al. 2003) The broadening of these lines, along with the slow
light-curve decline and high luminosity, suggest an interaction with the
circumstellar medium.

## 2.6 Supernova Rate Measurements

Recent measurements of the SN Ia rate at high-redshift (z\~0.5) are
based on samples of about 100 supernovae (Neil, et al. 2006).
Measurements of the Ia rate at low-redshift (z\~0.1) are based on very
small samples, generally rely on very large and uncertain efficiency
corrections, and are statistically discrepant with each other, likely
indicating a systematic error in one or more of the measurements. The
ARCSS+SDSS II sample, which will be large, homogeneously selected, and
have a well-understood and very high detection efficiency (based on
insertion of artificial supernovae into the data stream as well as
detailed Monte Carlo simulations), will enable a definitive measurement
of the SN Ia rate at low redshift. Dilday, et al. (2006) have already
made a first start on this, using \~20 SNe Ia at z\<0.12, where the
efficiency is very high, from the Fall 2005 SDSS II season to estimate
the low-z rate; the full ARCSS+SDSS II sample will be much more powerful
for this and allow more detailed studies. In combination with rate
measurements at higher redshift, it will enable us to test theoretical
models for the Ia progenitor population and to probe the evolution of
the SN Ia rate with \~10% precision. The sample will also be
sufficiently large that it can be used to study supernova rates of
different SN types as a function of, e.g., host galaxy properties (e.g.,
Sullivan, et al. 2006).

## 2.7 Testing and Improving Models of Type Ia Supernovae

A key feature of this project will be the tight, synergistic connections
between SN Ia observers and theorists. Three members of the team
(Khoklov, Lamb, and Cattaneo) lead the Flash Center for Predictive
Science, a proposed follow-on to the ASCI Flash Center,\
and have extensive experience in SN Ia modeling. The Flash Center aims
to use SNe Ia as a testbed for developing predictive science
methodologies able to handle large, 3-D, multi-scale, multi-physics,
integrated, and computationally demanding numerical\
simulations. As noted above, using SNe Ia to determine the properties of
dark energy will require 1% distance measurements per redshift bin. Most
scientists in the field believe that achieving this goal will require
accurate simulations of SNe Ia and quantification of the uncertainties
in the predictions made by such simulations.\
\
To accomplish these objectives and validate the simulations, a large,
unbiased, high-quality data set of SN Ia light curves and spectra, such
as ARCSS will provide, will be needed. An extensive set of such SNe Ia
is necessary in order to fully sample the range of SN Ia properties \--
key to understanding the SN Ia phenomenon, as well as to using SNe Ia to
constrain the properties of dark energy. The models will benefit
particularly from late-time (\> 2 months past maximum light)
spectroscopy, which probes supernovae as they enter the nebular phase,
providing a direct look into the inner iron-peak region that powers the
light curve. The ARCSS is therefore of crucial importance for the
success of the proposed Flash Center for Predictive Science; at the same
time, the interpretation and validation work carried out by the Flash
Center will enhance the importance and value of the data.

**2.8 Core-Collapse Supernovae**

In addition to SNe Ia, ARCSS+SDSS-II will discover and measure light
curves of a large number of nearby core-collapse type II and Ib/c
supernovae. These supernovae are important for probing star formation
and for understanding the physics associated with the deaths of massive
stars, as well as the chemical enrichment of interstellar and
intergalactic gas. Their average peak luminosities compared to type
Ia\'s are lower by \~1 to 2 mag (Richardson, et al. 2002), so the survey
will be sensitive out to z\~0.1 for these types. The local rate of
core-collapse supernovae, however, is believed to be higher than that of
Ia's by a factor of \~3 (Dahlen, et al. 2004), so a conservative
estimate based on Fig. 1 and on Fall 2005 SDSS II data (which included
17 spectroscopically confirmed, and an additional \~15 good photometric
candidate, core-collapse events) is that we expect to collect
high-quality light curves for at least \~80 core-collapse SNe in
ARCSS+SDSS II.

ARCSS will measure high-quality multiband light curves of nearby type
II-P supernovae, enabling us to build a library of II-P templates and
improve their utility as cosmic distance indicators (Hamuy and Pinto
2002; Nugent, et al. 2006). Roughly one third of all core-collapse
supernovae are expected to be of type II-P, so the survey will double
the current world sample of high-quality, low-redshift type II-P light
curves; this sample will be invaluable for future cosmological studies.

The unbiased wide-area coverage of ARCSS will also enable discovery and
detailed study of rare types of core-collapse events that have been
poorly sampled in previous surveys. One example of interesting rare
supernovae are the energetic Ib/c hypernovae, only six of which have
been studied in detail, and several of which now have firm associations
with long-duration gamma-ray bursts (e.g., Galama, et al. 1998; Iwamoto,
et al. 1998; Hjorth, et al. 2003; Stanek, et al. 2003). Detailed
spectral modeling and multi-epoch spectroscopic observations (including
very late spectra) of both normal Ib/c and hypernovae can constrain the
nature of the progenitor star and the physics and geometry of the
explosion, essential for distinguishing the physical conditions that
lead to GRBs (Maeda & Nomoto 2003; Proga, et al. 2003, Fryer & Warren
2004; Scheck, et al. 2006). Our collaboration, with expertise in
multi-dimensional radiation transport in SN ejecta, is well equipped to
carry out detailed modeling of these observations. Correlative studies
with other parts of the electromagnetic spectrum (in particular, radio,
X-ray, and gamma-rays) will be especially interesting for these types of
events that produce non-thermal emission in the central engine. As an
example, the Gamma-ray Large Area Space Telescope (GLAST), scheduled for
launch in late 2007, in concert with ARCSS will allow us to survey the
transient sky across a wide range of the electromagnetic spectrum and
will play an important role in unravelling the GRB-SN connection.

# 3. Schedule and Operations

The proposed schedule for ARCSS is to operate the 2.5m imager from Sept.
2008 through June 2009 and, as in SDSS II, to use most of the dark/grey
time and some of the bright time (excluding about 5 days around full
moon) over this period. In principle, the survey could run for a longer
period and/or, as in SDSS II, be interspersed with other programs,
though a contiguous supernova observing season should last a minimum of
three months to avoid a substantial efficiency hit in accumulating
well-sampled light curves. We prefer a continuous ten-month observing
run over interspersal with other large dark/grey programs, since we will
largely rely on the expertise, organization, and infrastructure we have
in place for the SDSS II Supernova Survey, and we anticipate that it
will be most efficient and practical to keep the team in place for this
period.

ARCSS will make use of the current infrastructure for data acquisition
and processing that exists at APO: the 2.5m imager, the data acquisition
system, and the compute cluster installed for rapid SN data processing
in summer 2005. We anticipate that the imager will be operated by one or
two trained observers per shift, under both photometric and
non-photometric conditions, as for the SDSS II Supernova Survey. Connie
Rockosi and Jim Gunn have agreed, in principle, to provide \`best
effort' camera maintenance expertise through the ARCSS period, with the
understanding that catastrophic failure is not covered. The compute
cluster will likely require the current level of on-mountain maintenance
it receives from APO staff; it runs the PHOTO pipeline through corrected
frames, carries out image frame subtraction in *gri*, and positionally
matches objects across the 3 filters. We expect the on-mountain compute
processing time to scale approximately with the number of pixels; since
the raw pixel rate will be ¾ that for sidereal scanning, we expect the
on-mountain cluster will be able to keep up with processing on a \<24 hr
timescale, as it has for SDSS II. The outputs will be transferred by
internet for further analysis by the collaboration, as they are now
(with roughly the same bandwith requirement). The Supernova team will
coordinate and carry out follow-up observations, as in SDSS II.
Currently, we re-process the data through the photometric pipeline for
public distribution at Fermilab.

# 4. Other Resources Needed 

Spectroscopic follow-up of ARCSS events will be needed for supernova
typing, redshift determination, and study of time-dependent spectral
features. The detailed follow-up strategy will depend, of course, on the
available resources and on the science interests of those with primary
access to those resources. Here we estimate the needed level of
spectroscopic resources for the science program described above and a
strawman plan that approximately satisfies it.

For ease of comparison with SDSS II, we artificially break the ARCSS
into 3 three-month observing quarters that happen to be contiguous (the
10^th^ month can always be added back in at the end). For each quarter,
we wish to obtain an average of 4 epochs of spectroscopy on the \~60
expected low-z Ia's with high-quality light curves and one epoch on the
remaining \~60 we expect to discover (see Sec.1). In the Fall 2005
campaign, about 75% of the unique, targeted SN candidates yielded
confirmed supernovae; since we expect at least this rate of success for
the lower-redshift ARCSS, we will need to take roughly 260 SN
spectroscopic exposures per quarter (this estimate does not include
additional time that might be desired for host galaxy spectra after the
SN has faded and does not include time for multi-epoch core-collapse
follow-up). This is the number of supernova-target spectra taken during
the Fall 2005 SDSS II campaign, so the basic ARCSS follow-up program
could be carried out if resources comparable to those that were
available for Fall 2005 are accessible for the ARCSS survey period.

To estimate the needed resources, we make the crude approximation that
the average telescope time per SN target, including overheads and time
spent on standard stars, will be one hour, roughly consistent with that
in Fall 2005. (Low-redshift supernovae near peak light will require less
time than this, those a few weeks past peak may take longer, and
detailed exposure times will of course depend on aperture, etc.)
Assuming typical 50% weather efficiency, this will require \~520
scheduled hours per quarter on non-queue-scheduled telescopes. (In Fall
2005, the queue-scheduled HET measured \~95 spectra in 90
queue-scheduled hours, while the ARC 3.5m measured \~64 spectra in \~124
hours, both consistent with the efficiency and exposure times above.)
This time should be spread among smaller aperture (2 to 3m) telescopes,
which can acquire good signal-to-noise spectra near peak light, and
those with larger aperture (4m and above), which can more efficiently
measure early and late spectra.

This required time appears to be reasonably well matched to the
estimated quarterly dark/grey resources we can plausibly expect to have
access to (though, in almost all cases, the access is competitive and
therefore not guaranteed):

-   HET (Stanford, Penn State): (\~30 hours queue) =60 hrs (non-queue)

-   MDM 2.4m (Ohio State 25% share): \~135 hrs

-   ARC 3.5m (UC+UW+NMSU+JHU+Director's Discretionary=25%):\~135 hrs

-   Subaru (JPG): \~30 hours (3 nights, based on 2005, 2006 allocations
    for SDSS II)

-   ESO or UK 3-4m (Portsmouth, European collaborators): \~60-200 hours
    (Fall 2005: 6 nights allocated on WHT 4.2m; Fall 2006: 17 nights
    allocated on NTT 3.5m plus 3 nights on NOT 2.5m)

While the uncertainty in these available follow-up resources is large,
the expectation based on two seasons of scheduled SDSS II follow-up is
that they could provide \~420-560 of the required 520 hours of needed
follow-up time. In addition, we can expect to have additional
collaborative access for follow-up to LBT (through Ohio State), SALT
(through collaborators at SAAO, U. Cape Town, Rutgers, and the HET
consortium), the CfA 1.5m (Kirshner group), and possibly Keck (through
possible collaborators at Berkeley and LBL), as well as applying for
public time on the CTIO or KPNO 4-m.

SDSS II has secured time for photometric follow-up on small telescopes
(NMSU 1m, UH88in, MDM 2.4m, VATT,...) in order to obtain late-epoch
photometry for a subsample of confirmed supernovae; we are likely to
continue this program for ARCSS, pending confirmation that the color
terms between different versions of the SDSS filters can be pinned down
to the level needed for accurate SN photometry (so called
S-corrections). We also expect to continue collaborative work with the
Carnegie Supernova Project, which is carrying out optical and NIR
photometry of selected confirmed SDSS II supernovae, and to broaden our
NIR follow-up through, e.g., the Liverpool Telescope. In addition,
members of the Supernova Legacy Survey (SNLS) team have expressed
interest in possible collaboration, though the scope is yet to be
determined.

# 5. Development Plan

The ARCSS will make use only of existing, tested hardware and, to the
extent feasible, existing software and will operate in a mode as similar
as possible to SDSS II in order to minimize development time and cost.
The main development tasks and those best positioned to carry them out
are: (a) devising a survey strategy that makes optimal use of the
telescope time (D. McGinnis, J. Marriner, Fermilab), (b) acquiring and
processing binned 2.5m test data in Fall 2006 to demonstrate that it can
be reliably and routinely acquired and processed (Fermilab and APO
staff), (c) acquiring and processing sidereal, photometric (i.e.,
\`survey quality') scans on stripe 79 in Fall 2006 or 2007, to complete
the needed template area (part of SDSS II operations), (d) upgrading the
data acquisition system to output GANG files in 2x2 binned mode (K.
Biery, Fermilab), (e) upgrading the focus loop to operate with 2x2
binned data (R. Lupton, Princeton), (f) upgrading the Skippy program for
telescope tracking with 2x2 binned data (J. Munn, USNO), (g) upgrading
and commissioning PHOTO at Fermilab to run with 2x2 binned data (J.
Adelman-McCarthy, Fermilab, in consultation with R. Lupton), (h)
modifying the on-mountain frame subtraction pipeline to operate with 2x2
binned data at the level of accuracy needed for SN target selection (A.
Becker, U. Washington), and (i) modifying the final photometry pipeline
so that accurate SN photometry can be extracted from 2x2 binned survey
data (J. Holtzman, New Mexico State).

Below, we summarize the preliminary feasibility studies that have been
carried out along these lines and what remains to be done. A detailed
work breakdown structure and schedule will need to be put in place,
along with a review schedule that will allow a timely decision about
whether to proceed with binned scans or revert to sidereal scans. We
estimate that the total effort needed to complete these development
tasks is approximately 3 FTE-years. If a negative decision on binned
scans were reached sufficiently early, the development effort could be
scaled down accordingly.

## 5.1 Survey Strategy

With the likely exception of one stripe in the south, the ARCSS will
carry out imaging of areas that have previously been scanned during SDSS
I and II. These earlier data, taken under photometric conditions in good
seeing at sidereal rate, will provide template images that will be
subtracted from the shallower ARCSS images to search for supernovae;
since they are deeper, better sampled, and taken under good conditions,
the templates will make only a small contribution to the noise in the
subtracted images. The template images will also provide star catalogs
for calibrating the subtracted images. From SDSS II, we already have the
machinery in place to turn SDSS images and associated catalogs into the
templates needed for frame subtraction.

It is likely that one more template stripe outside the current SDSS
footprint (stripe 79) will be needed in the south for the Fall observing
season. We will propose that two photometric nights with good seeing be
used in Fall 2006 or more likely 2007 to cover this region with a scan
at the sidereal rate, to ensure uniform template coverage of the survey
area.

To demonstrate feasibility, we have devised but not yet optimized a
strawman survey strategy. Scans are carried out over the existing SDSS
footprint (with the exception noted above) at three times sidereal rate,
with the constraint that the airmass be less than 2.0. These constraints
require us to cover typically 4 strips per night in about 2 sections per
strip (see Table 1). To achieve the desired cadence, we alternate
between the north and south strips of the target stripes from night to
night. With this strategy, we must repoint the telescope several times
per night; Fig. 7 shows an example scan of one section of a strip.

![](media/image9.png){width="2.9243055555555557in"
height="2.954861111111111in"}![](media/image10.png){width="2.8805555555555555in"
height="2.9166666666666665in"}

> Figure 7. Left: A strip to be scanned is shown (blue) superposed on
> the region with airmass\<2 (green circle at the beginning of the scan,
> red circle at the end of the scan). Magenta lines delimit the scan
> section, and the Milky Way is roughly delimited by the black lines.
> Right: the eight stripes to be surveyed.

As an example, we display the strawman survey plan for selected nights
in September, January, and May in Table 1. Note that we assume 15-minute
overhead for each telescope repointing and scan ramp-up.

Table 1a. Survey Area for Sept. 14-15 Table 1b. Survey Area for Jan.
14-15

  --------------- -------------------- ----------------- -----------------
  Stripe          Start Time           Stop Time         Scan Deg

  82              19:34                20:34             45

  86              20:49                21:49             45

  79              22:04                23:04             45

  76              23:19                0:19              45

  82              0:34                 1:34              45

  86              1:49                 2:49              45

  79              3:04                 4:04              45
  --------------- -------------------- ----------------- -----------------

Table 1c. Survey Area for May 14-15

  ---------------- -------------------- ----------------- -----------------
  Stripe           Start Time           Stop Time         Scan Deg

  17               20:29                21:42             55

  18               21:57                23:37             75

  16               23:52                1:05              55

  15               1:20                 2:27              50
  ---------------- -------------------- ----------------- -----------------

The strawman plan results in a mean scan area per night that is nearly 3
times the SDSS-II SN scan area, as desired, though the nightly scan area
varies considerably during the course of the survey (peaking in winter
and tailing off toward summer). With this strategy, the telescope is in
data-taking mode 83% of the available time between astronomical twilight
and sunrise; the remainder of the time is lost to telescope
repointing/scan ramp-up and to inefficiency near sunrise when the target
area drops toward the horizon, especially in May/June (in Table 3, the
last 75 minutes before sunrise are not used). Due to the latter issue,
one could consider ramping up another observing program that could use
the end of the night starting in late Spring.

One interesting feature of this plan is that the average number of
visits (before weather effects) for each region of the survey area is
63, compared to 45 for SDSS II SN. This should modestly improve the
efficiency for obtaining well-sampled light curves, because it implies
that \`edge effects' due to the finite survey duration will be smaller.
Since a given region will be visited over a timescale long compared to
that during which a typical SN is bright, it also suggests that some of
the ARCSS scans could be used to provide an improved model of the host
galaxy light and thereby improve final photometry. We believe the
strawman plan can be improved, and this will be one area for
development.

## 5.2 Calibration Strategy

Considerable effort has gone into improving the photometric calibration
of SDSS data, both by using multiple photometric runs on stripe 82
(Ivezic, et al. 2006) and by using the Apache Wheel cross-scans to tie
the entire survey together (ubercalibration, Padmanabhan 2006).  These
efforts have demonstrated that we can achieve uniform photometric
zeropoints across the sky at the \~1% level.  Since ARCSS will overlap
with existing survey data and cross-scans, we can take advantage of this
excellent photometric calibration.  (Depending on the survey strategy
adopted, additional cross-scans might be necessary, but this would be a
small part of the total survey observations.) A fraction of the ARCSS
scans will be photometric and might be used to further refine the
calibration.

C. Stubbs has developed a laser photometric calibration system that has
been tested on the CTIO 4-m; plans are underway to bring it to the ARC
2.5m in 2007. If successful, this could provide an independent
calibration of the SDSS photometric system and would be beneficial in
combining SDSS results with other surveys.

**5.3 Instrument Operations**

An important part of our development plan will be to demonstrate that
the telescope, camera, and data acquisition system can operate reliably
in 3 times sidereal scanning mode with 2x2 binned readout. A number of
faster, 4x4 binned Apache Wheel scans have been taken and successfully
reduced as part of the ubercalibration effort. Run 3868, a test run in
April 2003 with poor seeing (\~2") and partial moon, was taken at 3
times sidereal with 2x2 binned readout and partially processed through
the photometric pipeline (see below).

We propose to allocate a modest amount of time during the Fall 2006
Supernova Survey for a test run in 3xsidereal, 2x2 binned mode to ensure
that we understand all the operational issues as well as to perform an
end-to-end test of the resulting SN photometry. During October, if the
weather is good during the entire night, we can finish scanning stripe
82 before the end of the night; when that happens, on one or two such
nights we would repoint the telescope on the same strip and carry out a
faster scan of a portion of the area covered earlier that night. The
total observing time allocated to this test would be 2 to 3 hours.

While not strictly required, we strongly favor modifying the existing
monitoring tools to work with 2x2 binned data. In particular, we would
like to make sure that we can produce Gang files for analysis and that
Skippy and the Focus Loop can read the files and produce appropriate
results.

## 5.4 Pipeline Operations

In addition to demonstrating that the camera can be operated and data
acquired in faster, binned mode, we will also use the test run to ensure
that (a) PHOTO and the frame subtraction pipeline deliver preliminary SN
photometry with the accuracy needed to identify SN candidates for
follow-up and (b) our final photometry pipeline delivers SN photometry
with the accuracy needed to meet our science requirements.

The statistical S/N penalty associated with larger pixels, while not
desirable, should be tolerable. The effective size of the PSF will
increase with 2x2 binning and therefore the measurement of the supernova
flux will be contaminated by more sky noise. A rough estimate may be
made by adding the rms pixel size (for 2 pixels, it
is$0\text{.}8\text{\} slash \{ sqrt \{}12\text{\} \} =0 }.\text{ }23$)
in quadrature with typical good seeing of 0.5" (standard deviation, not
FWHM). The result is that the effective sky noise is increased by \~20%.

We have performed some preliminary tests that indicate that the frame
subtraction pipeline will yield adequate photometric accuracy to carry
out the SN search in binned mode. While the larger binning does not
affect the number of photons detected, one might worry that the image
undersampling with larger pixels could result in significant photometry
errors due to inadequacies in the PSF fitting procedure (especially when
the seeing is good). We first tested this with the portion of the frame
subtraction pipeline that determines zero-points. This code finds the
zero-point and also calculates the rms deviation of the PSF magnitudes
of the calibration stars around their catalog values (the latter from a
photometric run). We simulated 2x2 binning by rebinning existing stripe
82 SN data and comparing the spread of PSF mags vs. catalog mags with
that for unbinned processing of the same run. The results are shown in
Table 2. We see that the rms spread in PSF mags does not increase with
2x2 binning, which indicates that the PSF photometry model used by the
frame subtraction pipeline is not systematically degraded by the
binning.

Table 2. Dispersion of PSF Mags in binned vs. unbinned images

+-----------------+-----------------+-----------------+-----------------+
| Frame           | Filter          | Unbinned        | 2x2 binning     |
|                 |                 |                 |                 |
|                 |                 | (rms in         | (rms in         |
|                 |                 | magnitudes)     | magnitudes)     |
+-----------------+-----------------+-----------------+-----------------+
| 529             | *g*             | 0.024           | 0.025           |
+-----------------+-----------------+-----------------+-----------------+
|                 | *r*             | 0.036           | 0.038           |
+-----------------+-----------------+-----------------+-----------------+
|                 | *i*             | 0.035           | 0.036           |
+-----------------+-----------------+-----------------+-----------------+
| 530             | *g*             | 0.036           | 0.036           |
+-----------------+-----------------+-----------------+-----------------+
|                 | *r*             | 0.028           | 0.027           |
+-----------------+-----------------+-----------------+-----------------+
|                 | *i*             | 0.031           | 0.031           |
+-----------------+-----------------+-----------------+-----------------+

We performed a similar test using a run with binned data, run 3868. This
run had 2x2 binning and a scanning rate of three times sidereal, the
proposed mode for ARCSS, but conditions were moony with highly variable
seeing. The data were processed through PHOTO at Princeton to produce
uncalibrated object catalogs. Matched to objects in the Catalog Archive
Surver, this data did show a \~50% increase in scatter in PSF mags for
bright objects compared to that for an unbinned, photometric run. While
there is lower precision with binned data, the software yields
reasonable results, and the resulting scatter (a few %) is small
compared with the 15% individual supernova variation.

While the above test concerns point-source photometry for isolated stars
in binned scans,

the undersampling of the supernova images will likely be a more
important issue in subtracting the galaxy background light. To test
this, we have run our standard frame subtraction pipeline on Fall 2005
SDSS II images of a supernova at z\~0.1. In Table 3 we compare the
unbinned results with those obtained by rebinning the search data in 2x2
format.

Table 3. SN flux measurements in binned vs. unbinned images

+------+------+-------------+-------------+------------+-------------+
| Run  | Fi   | Flux        | Flux        | Difference | Difference  |
|      | lter |             |             |            |             |
|      |      | (Normal     | (2x2 bins)  | (%)        | (std        |
|      |      | bins)       |             |            | deviations) |
+------+------+-------------+-------------+------------+-------------+
| 5792 | *g*  | 961±33      | 942±33      | −2.0       | −0.58       |
+------+------+-------------+-------------+------------+-------------+
| 5792 | *r*  | 2154±43     | 2278±46     | 5.7        | 2.87        |
+------+------+-------------+-------------+------------+-------------+
| 5792 | *i*  | 1809±46     | 1847±50     | 2.1        | 0.82        |
+------+------+-------------+-------------+------------+-------------+
| 5800 | *g*  | 1008±38     | 1005±39     | −0.3       | −0.08       |
+------+------+-------------+-------------+------------+-------------+
| 5800 | *r*  | 2030±48     | 1988±48     | −2.1       | −0.89       |
+------+------+-------------+-------------+------------+-------------+
| 5800 | *i*  | 1788±52     | 1726±52     | −3.5       | −1.20       |
+------+------+-------------+-------------+------------+-------------+
| 5813 | *g*  | 862±32      | 849±32      | −1.4       | −0.39       |
+------+------+-------------+-------------+------------+-------------+
| 5813 | *r*  | 1777±38     | 1871±40     | 5.3        | 2.52        |
+------+------+-------------+-------------+------------+-------------+
| 5813 | *i*  | 1548±41     | 1565±25     | 1.1        | 0.41        |
+------+------+-------------+-------------+------------+-------------+

This test indicates that the frame subtraction photometry with 2x2 bins
is typically within a few percent of that for unbinned data, for this
object; for rapid selection of supernova candidates for spectroscopic
targeting, we have found that systematic SN photometry errors of up to
10% can be tolerated (and were often seen during the 2005 run),
indicating that the frame subtraction pipeline will not need
modification to carry out the ARCSS search.

Final SN photometry, of course, must be more precise than this. We
expect to carry out final ARCSS photometry with a version of the "scene
modeling" pipeline written by Jon Holtzman for SDSS II SN analysis,
suitably modified for binned scans. A preliminary test, similar to that
shown in Table 3, using scene modeling PSF photometry on rebinned vs.
unbinned scans of another low-redshift SN Ia from 2005 shows similar
scatter at the few % level. Development work will be needed to study the
dependence of this scatter on seeing and on relative galaxy surface
brightness and to see if it can be reduced to the required \~1% level.
If it can, then ARCSS would be carried out in binned mode; if not, we
would use sidereal scanning in unbinned mode.

# 6. Cost Estimate

We have developed an estimate of the person-years of equivalent effort
needed for both development and operations, based on consultations with
Steve Kent and Bill Boroski. To carry out the development tasks listed
at the beginning of Sec. 5, we estimated 3 FTE-years, comparable to but
somewhat larger than that needed for SDSS II SN. We have based the
operations estimate on current manpower usage at APO and Fermilab, as we
do not expect that to change substantially. Operation of the imaging
pipeline and associated support at Fermilab uses 2.25 FTE, IOP support
is 0.5 FTE, and data distribution is 2 FTE; rapid SN data processing
(currently personnel at U. Chicago and U. Washington) uses about 0.8
FTE. Since there will be no spectroscopic operations, telescope
engineering could be reduced to 2.75 FTE during this period. We assume
project management at the current level of 0.5 FTE would continue to
ensure smooth running of the project, and we assume 0.5 FTE for routine
camera maintenance beyond that covered by the on-site engineers.
Assuming a suite of 6 observers assigned to 2.5m operations, the total
development and operations effort required is approximately 16
FTE-years; for the cost estimate, we conservatively assume this will all
be supported by the project, although a fraction of the development work
(that by senior scientists) will in fact be in-kind. Assuming an average
of \$130K per FTE-year (including benefits), the total estimated project
cost for development and operations would amount to \$2.1M. Note that
this does not include any subsidization by the project of the additional
costs of operating APO (beyond the immediate costs, noted above, of
operating the 2.5m). Note that elimination of the development costs for
binned scanning would reduce the bottom line by \~\$390K, or about 20%.

We have not included the spectroscopic follow-up time that will be
contributed by members of the collaboration, since that will not be a
cost to the project, but a rough extrapolation from ARC 3.5m operating
costs suggests it should be valued at about \$1M; indeed, the ARCSS will
be leveraging those resources. We have also not included the personnel
costs associated with coordinating, taking, and reducing the
spectroscopic follow-up data, since we expect that to be effectively
in-kind contributions from the team, as in SDSS II.

Since this project will be directly supporting the goals of JDEM and
helping fulfill the mandate set out by the Dark Energy Task Force, we
would expect to approach the U.S. funding agencies for possible support.
We have not explored the possibilities of partial funding from
participating institutions in depth, though more than one has expressed
willingness/interest in considering it. We would also be open to
exploring options for private funding.

# 7. Public Data Release

The ARCSS data will be released in various levels and stages. First, we
plan to make basic information about all SN candidates we identify
immediately available via a public alert web page; we also plan to
announce candidates through automated e-mails using the nascent VOEvent
Net. Since our sample will be relatively bright, we also will likely
issue electronic telegrams before (as well as after) they are
spectroscopically confirmed. On the timescale of \~a month, we would
expect to release corrected image frames and uncalibrated object
catalogs in a simple database, as we have done for SDSS II SN. For SDSS
II, we are also developing the infrastructure needed to apply
calibrations to the non-photometric runs and expect to make calibrated
object catalogs for them available soon. We plan to carry that forward
to ARCSS, so that calibrated object catalogs can be made available on a
timescale of \~months. This will constitute a very substantial,
wide-area data set for general astronomical studies of transient and
variable phenomena. On a longer timescale, of order a year, we will
publish our final SN photometry. There may be interest in producing
co-added, 2x2 binned images for the ARCSS area, since the median
co-added exposure time will be \~10 times that of SDSS, but we have not
included that in the proposed project.

# 8. Personnel

We expect to operate as a collaborative team in a manner similar to the
SDSS II SN project. The proposal team includes most/all of the SDSS II
SN science team plus a number of new people at other institutions
interested in participating. The people expected to be principally
involved in the development work have been identified above in Sec. 5.
We expect that 2.5m observing will be carried out by APO staff,
following the SDSS model. Telescope and instrument engineering and
off-site data processing for the 2.5m are currently carried out by
Fermilab staff, paid for by the project. These staffing arrangements
need to be resolved in the context of the post-SDSS-II management and
funding approach. As in SDSS II, the scientists on the team will be
involved in and responsible for day-to-day data 2.5m image processing on
the mountain, selection and coordination of spectroscopic targets,
proposing for, carrying out, and analyzing spectroscopic follow-up
observations, and overall science analysis.

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