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ADASS 2002 Conference Proceedings Next: Calibration of COS data at STScI
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Grumm, D. M. & Casertano, S. 2003, in ASP Conf. Ser., Vol. 295 Astronomical Data Analysis Software and Systems XII, eds. H. E. Payne, R. I. Jedrzejewski, & R. N. Hook (San Francisco: ASP), 199

Self-calibration for the SIRTF GOODS Legacy Project

D. Grumm and S. Casertano
Space Telescope Science Institute, Baltimore, MD 21218

Abstract:

Data analysis for the SIRTF GOODS Legacy Project must be able to achieve a level of calibration noise well below a part in 10,000. To achieve such a high level of fidelity, a form of self-calibration may be required in which the sky intensity and the instrumental effects are derived simultaneously. Two methods being investigated are a least squares approach based on the work of Fixsen and Arendt at GSFC, and an iterative method. Both methods have been applied to derive the sky, flat field, and offset from simulated data for instruments to be flown on SIRTF; the results will be discussed.

1. Introduction

The Great Observatories Origins Deep Survey (GOODS) incorporates a SIRTF Legacy project designed to study galaxy formation and evolution over a wide range of redshift and cosmic lookback time. Our current understanding is that the standard pipeline developed by the SIRTF Science Center may not achieve the levels of fidelity required for the analysis of the deepest GOODS data, which translate into a level of calibration noise well below a part in 10,000. Self-calibration may be required to achieve the necessary level of calibration.

2. Algorithms

Two algorithms have been used to simultaneously solve for the sky, gain, and offset for simulated sets of dithered images. These techniques are the Fixsen-Arendt least squares self-calibration code (Arendt et al. 2000) and an iterative code.


Fixsen-Arendt code:



Iterative code:


Compared to the Fixsen-Arendt technique, the iterative approach may scale more favorably with dataset size and complexity of the observing process (i.e., presence of instrumental artifacts), and is less memory intensive. In the iterative algorithm, the sky and gain are alternately updated, as shown in Figure 1.

Figure 1: Iterative self-calibration method.
\begin{figure} \epsscale{.75} % orig .8\plotone{P7.6_1.eps} \end{figure}

3. Approach

In lieu of actual data, we've used SIRTF's MIPS instrument simulator1 to generate truth images. The simulator generates images which include sky background, Poisson noise, readout noise, and dark current. The MIPS truth image (316$\times$453 pixels) is shown in Figure 2; 94% of the pixels are greater than 0.01% above the background.

From a truth image, sets of individual images (128$\times$128 pixels for MIPS) were generated from a table of integer dither positions. For a self-calibration run in which the sky and gain are to be derived, each individual image is multiplied by the input gain. (If the offset is also to be derived, it is also incorporated). The input gain image used has $\pm$30% large-scale variation, and $\pm$5% rms pixel-to-pixel variation.

Figure 2: MIPS truth image, and full MIPS dither pattern
\begin{figure} \epsscale{.80} \plottwo{P7.6_2.eps}{P7.6_3.eps} \end{figure}

4. Results

For the sky and gain runs, the goodness of fit was quantified by comparing the derived gain to the input gain. Tests were done by varying the dither pattern, varying the number of dither positions, and varying the tightness of the pattern. The full MIPS dither pattern of 1440 pointings (6 major pointings with 18 minor pointings each) is shown in Figure 2. Several dither patterns were compared for a subset of these observations.

With a poor dither pattern (4 sets of only 3 chosen positions from the 18-point Reuleaux pattern), the gain ratio shows vertical artifacts due to an insufficient number of x-positions. With a better dither pattern (4 sets of 7 chosen positions from the 18-point Reuleaux pattern), there are no visible artifacts in the gain ratio.

Using the full MIPS dither pattern shown in Figure 2, the derived sky and gain have no unexpected features. The sky is reproduced with the expected noise level, and is within a few percent of the combined shot and read noise. The derived sky noise and the gain ratio for this case are shown in Figure 3.

Figure 3: Sky noise (rms=1.5E-4) and gain ratio (rms=6.4E-5)
\begin{figure} \epsscale{.80} \plottwo{P7.6_4.eps} {P7.6_5.eps} \end{figure}

5. Discussion

Our tests indicate that for reasonable dithering strategies, the results of the derived sky and gain are close to shot-noise-limited sensitivity. If there are too few dither positions, periodic artifacts are introduced into the derived quantities at approximately the 1-sigma level. If the dither pattern is too tight, the large-scale variation in the gain is not constrained.

The iterative method offers flexibility to incorporate additional instrumental effects which may occur in the actual data. We are currently modifying the routine to accommodate subpixel dither positions and geometric distortion.

Acknowledgments

We are grateful for many fruitful discussions with Rick Arendt, Richard Hook, and the GOODS team.

References

Arendt, R. G., Fixsen, D. J., & Moseley, S. H. 2000, ApJ, 536, 500


Footnotes

... simulator1
Ranga-Ram Chary, private communication
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Next: Calibration of COS data at STScI
Up: Calibration
Previous: Calibration of BIMA Data in AIPS++
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