1 / 23

Understanding Reference Pixels for JWST NIR Detector Calibration

This article provides an overview of the importance of reference pixels in calibrating the NIR detectors for the James Webb Space Telescope. It discusses different calibration methods, including spatial and temporal averaging, and explores various detector characteristics such as dark current, read noise, linearity, and quantum efficiency.

lstreet
Download Presentation

Understanding Reference Pixels for JWST NIR Detector Calibration

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Reference pixels and readout modes: What we have learned thus far Don Figer, Bernie Rauscher, Mike Regan March 13, 2003

  2. 4 1.E+02 Sunshield Dark current = - 0.126 e /sec 1.E+01 - 0.020 e /sec Spectra 3 JWST requirement - 0.003 e /sec 1.E+00 Images Duration of DRM NIR Observations [yrs] JWST goal Signal [e-/sec/pix] 2 1.E-01 R=5 1.E-02 Zodiacal Light 1 1.E-03 R=1000 0 1.E-04 2 4 6 8 0.1 1 10 Wavelength [mm] Read noise per exposure [electrons] Detectors Are Important for JWST

  3. NIR Detector Characteristics • Dark current • Read noise • Linearity • Latent charge (persistence) • Quantum efficiency (QE) • Intra-pixel sensitivity • Thermal stability • Radiation immunity

  4. IDTL Test System Controller Electronics Dewar Entrance Window Vacuum Hose He Lines

  5. JWST MIR Detector Requirements

  6. Dark Current • Lowest measured dark current is ~0.005 e-/s/pixel.

  7. IDTL Measurements: Read Noise • Read noise is ~10 e- for Fowler-8. (system read noise is ~2.5 e-)

  8. Raytheon 1024x1024 MIR MUX Raytheon 2Kx2K NIR Module Rockwell 2Kx2K NIR Module Reference Pixels • All candidate JWST detectors have reference pixels • Reference pixels are insensitive to light • In all other ways, designed to mimic a regular light-sensitive pixel • NIR detector testing at University of Rochester, University of Hawaii, and in the IDTL at STScI -> reference pixels work! • Reference pixel subtraction is a standard part of IDTL data reduction pipeline

  9. Use of Reference Pixels • JWST’s NIR reference pixels are grouped in columns and rows • Most fundamentally • reference pixels should be read out in exactly the same manner as any “normal” pixel • data from many reference pixels should be averaged to avoid adding noise to data • We have begun to explore how reference pixels should be used. Approaches considered include the following. • row-by-row subtraction • maximal averaging (average all reference pixels together and subtract the mean) • spatial averaging • temporal averaging • Spatial averaging is now a standard part of IDTL calibration pipeline

  10. A Picture of IDTL System Noise • Shorting resistor mounted at SCA location • 1/f “tail” causes horizontal banding. • Total noise is =7 e- rms per correlated double sample.

  11. After Before Averaging small numbersof reference pixels adds noise • Averaged the last 4 columns in each row and performed row-by-row subtraction

  12. Spatial Averaging This is a standardpart of the IDTL datacalibration pipeline • In spatial averaging, data from many (~64 rows) of reference pixels are used to calibrate each row in the image • A Savitzky-Golay smoothing filter is used to fit a smooth and continuous reference column • This reference column is subtracted from each column in the image • Using this technique, we can remove some 1/f noise power within individual frames • In practice, this technique works very well

  13. Spatial Averaging: Before & After Before After

  14. Temporal Averaging • Dwell on the reference pixel and sample many times before clocking next pixel • Potentially removes most 1/f • Not tried this in IDTL yet. U. Hawaii has reported some problems with reference pixels heating up

  15. Temporal Averaging: Before & After Before After

  16. Summary of Reference Pixel Calibration Methods • Spatial averaging works well using a Rockwell HAWAII-1RG detector • Based on conversations with U. Rochester, we foresee no problems with SB-304 • Temporal Averaging is promising. More work needed using real detectors.

  17. Summary • Reference pixels work and are an invaluable part of the data calibration pipeline • We have explored three techniques for using reference pixels • row-by-row subtractions, • maximal averaging, • spatial averaging, & • temporal averaging • Averaging at the end of row will not work • Spatial averaging works well and is robust • We have found: • dark current is low (~0.01 e-/s/pixel) • glow is very small • noise goes down as roughly 1/root(N) up to 8 reads (at least) • persistence is observed • JWST requirements seem realizable • saving all the data are necessary to mitigate unforeseen detector effects, such as the non-linear bias drift after reset ("shading" in NICMOS). Note that ref pixels do not get rid of all of the effect. • Cosmic ray rejection requires careful handling of reference pixels, output voltage drifts, and knowledge about previous history (persistence)

  18. Appendix

  19. NIR Detector Effects - NICMOS • Dark current • Bias drifts • QE variations • Amplifier glow

  20. NIR Detector Effects - NICMOS • Persistence

  21. NIR Detector Effects - NICMOS • DC bias level drift • Ghosts

  22. NIR Detector Effects - NICMOS • Linearity • Well depth

  23. NIR Detector Effects - NICMOS • QE • Dark current “bump”

More Related