Page History

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1. Training – Run the aXe WFC3 Jupyter-notebook Cookbook
2. Refactor grizli | pyLINEAR | Nor's code to use JWST version of grismconf?
   1. refactor HST reference files / geometric distortion info
   2. refactor XXX to use JWST grismconf
   3. compliant gwcs for all code bases 
3. All code bases outputs 1D and 2D in formats compatible with specutils
4. Fork the JWST pipeline as a basis? 
   1. Compare JWST CALWEBB_SPEC2/3 steps to what aXe and grizli, etc are doing.
5. Assess performance issues of grismconf vs. JWST pipeline version?
6. Jupyter notebook illustrating how to do the equivalent aXedrizzle without using aXe?
   • E.g. reproduce cell 7 of the WFC3 aXe cookbook without using Astrodrizzle
     • Possibly first converting WCSs to gWCS to include distortion info?
   • Using reproject package (doesn't yet have drizzle algorithm though)
   • Using JWST pipeline version of drizzle
   • JWST pipeline is doing the co-adds in 1D space 
     • not currently in 2D extractions, but this should be possible with the current code
7. Reproduce steps of aXeprep in a Jupyter notebook using Astropy ecosystem.
   1. There is a prep module in grizli that has a lot of useful functionality that isn't upstream
   2. Look at tools there that could be pushed upstream
      1. e.g. image alignment – not all grism specific
8. Write a summary of science use-cases for each of the grism packages
   1. exists already in roadmap? SSG webpage? WFC3 web pages?
9. Illustrate in a Jupyter notebook how to do a WFC3 forward-model simulation without using aXeSIM
   1. E.g. using grizli or SBE, which might eventually be refactored to use a common grismconf
   2. Or using aXeSIM
   3. Or using specutils + synphot?
      1. Does specutils support the full geometric transformation pipeline that the JWST pipeline does, already, since it supports gwcs?
      2. Does synphot in principle support applying the throughput?
10. Illustrate in a Jupyter notebook how to do flat fielding without using aXe
    1. E.g. using JWST pipeline machinery
11. Illustrate in a Jupyter notebook how to flag contamination without using aXe.

Use-Cases / Workflows

1D spectral extraction for perfectly registered & perfectly calibrated data

Story: As an impatient astronomer, I just want to extract a 1D spectrum for a single source given perfectly registered images and assuming calibrations are correct. I want it fast and I don't want to think about forward modeling.

What do I need as inputs?

• Direct and slitless images with perfect WCSs
• catalog information (coordinates, magnitudes ...)
• Exposure times?
• Noise model? Or components of the noise model (gain, exptime, read noise, subtracted background)?
  • Or is this already in an uncertainty array?
• Detector signatures already removed (e.g. CALWEBB_DETECTOR1)
• Identification of the pixels in the direct image corresponding to "the source" (e.g. from a segmentation map)
• Identification of any pixels in the direct image corresponding to contaminating sources

What does this step do for me?

• Maps pixels in the direct image to x,y, wavelength
• Accumulates the flux at each wavelength (onto some wavelength grid)
  • Perhaps weighted by the flux in the direct image at this cross-dispersion location
• Accumulates uncertainties at each wavelength
• Accumulates some kind of contamination flag at each wavelength

Why would I not want to do this?

• This doesn't do much to address contamination
• This mixes together wavelength and position more than a forward-modeling approach. 
• Uncertainties are likely to be quite misleading

Simulation/Model based extraction of 1D spectrum for perfectly registered and calibrated data taken at several different orientations

Story: As a careful observer, I have taken my data at several different orientations with direct images as well. I would like to extract 1D spectra taking advantage of the observed morphology of the source in the direct image, and a first guess at its spectrum and that of the sources that may be overlapping.

What do I need as inputs?

• Direct and slitless images with perfect WCSs
• catalog information (coordinates, magnitudes ...)
• Exposure times?
• Noise model? Or components of the noise model (gain, exptime, read noise, subtracted background)?
  • Or is this already in an uncertainty array?
• Detector signatures already removed (e.g. CALWEBB_DETECTOR1)
• Identification of the pixels in the direct image corresponding to "the source" (e.g. from a segmentation map)
• Identification of any pixels in the direct image corresponding to contaminating sources

What does this step do for me?

• Maps pixels in the direct image to x,y, wavelength 
• Folds the model spectra through the instrument response 
  • for the target of interest and the contaminants
  • for each orientation
• Fits the models to the data
  • Somehow? 
    • How do we specify parameters to vary?
    • What do we use as the merit-function for the fit? 
    • How do we weight the data?
    • What do we use as the optimizer?
    • How do we calculate uncertainties?
• Accumulates some kind of contamination flag at each wavelength

Why would I not want to do this?

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Epics

Identify and match data sets

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