NICMOS Bias Dependent Calibration

Pirzkal N., Dahlen T., Bergeron E., Wiklind, T. and the NICMOS TeamSpace Telescope Science Institute, Baltimore, MD, USAABSTRACT
We present our latest calibration work of the NICMOS instrument on the Hubble Space Telescope. This new calibration method uses a novel
method to determine the Q.E. of the detector, expressed as an effective temperature called biastemp, directly from the science exposure.
This has allowed, as we show here, to better monitor the behavior of the instrument's three detectors, providing calibration files that accountfor changes in photometry, flat-fields, and different components of the dark signal, including the shading and the amplifier glow. It has also
allowed us improve the calibration of this instrument by providing bias level specific calibration files such as flat-fields, darks, and shading.
We express this as an idealized temperature (biastemp) of the instrument which is determined from the bias level of the data, and which isdetermined using the first read of these non-destructive read, multi-accum datasets. It has allowed us to determine this biastemp value ofeach of the three NICMOS detectors to within 0.05K and offers a significant improvement over the available mounting cup temperature
sensor that has been used in the past, as it is as precise but is a direct measurement of the state of the detector, whether truly due to achange in temperature or internal voltage supply.

GOAL
The NICMOS detectors on HST are extremely sensitive to changes in operating temperature and supply voltage. These temperature andvoltage variations affect quantum efficiency, dark current and bias in ways that limit overall sensitivity and make calibration difficult. We
assume here that the voltage supplied to the detectors is constant, which is not known, and express observation as an idealized temperature(biastemp) that assume no other changes but that of temperature.

TEMPERATURE AND BIAS LEVELS
Each detector quadrant has its own seperate bias versus mounting cup temperature
relationship, with similar slopes but different zeropoints. This gives 4 independentmeasures of temperature from bias per readout, each with its own noise characteristic.
The RMS for these measurements are +/- 0.3, 0.7, 0.5, and 0.1K for the four quadrants,respectively (Figure 1).

VOLTAGE AND BIAS LEVELS
The bias level of each quadrant is affected by fluctuation in the voltage level ofthe instrument from the NICMOS LVPS, a constant-current power supply. If thevoltage supplied by the spacecraft power bus changes, the swithing LVPS inNICMOS compensates for it. When the spacecraft enters orbits, the batteries
begin to drain and the bus voltage drops. The LVPS compensates by changing
the width of the switching pulse but this results in slightly different bias level in agiven NICMOS exposure. During the process of data-reduction, the zeroth/bias
read of a NICMOS non-destrutive series of reads is subtracted so that this effectdoes not affect the science content of the exposure. But this variation in bias level
imposed by the LVPS complicates using the bias level as a proxy for temperature
measurement. The LVPS states are however quantized into 7 states in eachquandrant. One way to solve this problem is to compare two quandrant which arein phase together. Plotting the mean bias level of each quadrant against the otherthree reveals a series of ellipses showing the 7 quantized “states” (Figure 2).
Subracting off the sinusoidal LVPS signal corrects all the states to a commmon
bias. Residuals are temperature induced. After correcting for possible voltageinduced phase, the bias dependence is only temperature dependent. The typicalaccuracy reached after applying this correction is 0.05K (Figure 3). While we talkhere of a real temperature, this assume no other changes in LVPS voltages, whichmight not be appropriate and would result in a change in detector Q.E. that would
mimic a physical temperatur echange.
IMPLEMENTATIONThe NICMOS temperature from bias method had been implemented in Python is
distributed as part of the STSDAS/NICMOS Pyraf package. The task is called
CalTempFromBias and can be ran by the user. It is now part of the routine
NICMOS pipeline calibration and is used to select the best data calibration files.

What is NICMOS?
NICMOS is a cooled, internallycorrected, near-IR imaging camera
which takes advantage of HSTʼsstable optics and dark on-orbit
backgrounds to produce filteredimaging with high sensitivity andStrehl ratio, low resolution (R~200) multi-object grism
spectroscopy, coronagraphy, andpolarimetry.
The threeindependent NICMOS cameras
offer fields of view ranging fromsmall to medium, and angularresolutions from high to moderate.

FPA temperature sensors.
These are not used dur tothermal emission, high noise,and random bias level after
power cycling.
The nearby “mounting-cup”
sensors are on an aluminum
bracket that this board isconnected to.

Figure 1

Figure 2Figure 3

COMPARISON WITH MOUNTING CUP TEMPERATURE
Comparing the temp-from-bias derived tempeature versus the temperature
measured by mounting-cup sensor shows that while the mounting cup temperature
has been kept constant, the biastemp of the detector has been going down (Figure4). We assume that this change of biastemp is the reason for any observed
variation of the Q.E. of the detector. In the past, darks and flat-fields weregenerated for specific observing dates. This is appropriate for a slowly varyingsystem. The temp from bias method allows us to now match biastemp specific flatfields and darks to the measured biastemp of given exposures.
EFFECT ON NICMOS FLATFIELDS
The following few figures compares the use of temporal, or epoch, flatfield and ofbiastemp flatfields. The biastemp flatfields result in a significant improvement in theability of the flatfields to correct for Q.E. variation across the detector. The figureon the left shows the ratio images obtained by dividing each SCI extension in NIC3,
F110W temperature dependent flat by the SCI extension in the static flat file, for pre andpost-NCS data respectively. The biastemp goes up as we go from panel 1 to panel 5.
Panel 3 in figure 6a and panel 4 in figure 6b are relatively flat due to the fact that thetemperature range of SCI[11] image is close to the biastemp of the static flat file. From
both the figures we can conclude that the biastemp flat-fields do have some structure ascompared to the static flat-field and the structure of flat field does depend upon
temperature.

Figure 6a

Figure 6bFigure 4
Figure 5 shows the measured Q.E. response as a function of biastemp,
normalized at T=63K. Curves are shown for wave-lengths 1.1, 1.6 and 2.2
micron. Shorter wave-lengths have steeper slopes.

Figure 5

CONCLUSION
RELATED INFORMATION AND FURTHER READING
Data obtained using NICMOS have been known to require careful choice of flatfield and dark current images in order to• Begeron, E. 2009. NICMOS ISR, in prep
properly calibrate observations. This has traditionally be done using “epoch” data calibration products as the mounting • de Jong, 2009, NICMOS ISR, in prep
• Pirzkal N., Grumm, D., Bergeron, E., ISR NICMOS 2009-007, STScI, CalTempFromBias: Implementation andcup sensor showed no sign of any temperature variation in the instrument.
Testing
The novel approach of determining the state of the dectector using the bias level of the detector itself as data is taken • Dahlen, T., Barker, E., Bergeron, E., Smith, D. ISR NICMOS 2009-002, STScI, Temperature Dependent Dark
Reference Files: Linear Dark and Amplifier Glow Components
has however shown that the Q.E. of NICMOS detectors has varied significantly over the last few years, mimicking theD., Dahlen, T., ISR NICMOS 2009-005, STScI, NICMOS Temperature Dependent Flat-fields
effect of a gradual lowering of detector temperature. While we cannot rule out that this effect is not solely temperature •• Thatte,Dahlen, T., McLaughlin, H., & de Jong, R. 2007, NICMOS ISR-2007-002, STScI, NICMOS Time Dependent Flatfields
driven but migth also be voltage driven, the effect is similar and we are now able to directly measure the state of theNICMOS detectors for any given datasets with a biastemp accuracy of 0.05K. This has allowed us to compute • Thatte, D. and Dahlen, T. et al. 2009, “NICMOS Data Handbook”, version 8.0, (Baltimore, STScI)
biastemp dependend calibration products that are improving the NICMOS data calibration significantly as NICMOS
calibration products can be better matched to NICMOS observations.