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Goals

  • Retire aXe
  • Retire aXeSIM
  • Leverage the JWST infrastructure
    • just crds, gwcs and asdf?
  • Enable common "Grismconf" infrastructure for:
    • pyLINEAR
    • grizli
    • EM2D, and Nor's other modules
    • JWST pipeline
    • (WFIRST)
  • Ensure that users can do all the flavors of geometric transformations (and can understand them)
  • Make sure calibration (files) are consistent with the approach to geometry
  • Support the following HST modes:
    • WFC3/IR
    • ACS
    • WFC3/UVIZ?
  • Compatible with JWST pipeline outputs of WFSS modes (NIRISS and NIRCam)

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
  • 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

Epics

Identify and match data sets

  • Should this be part of Astrogrism, or outside the package (e.g. in a notebook, as a tutorial)?
  • Archive queries via API for both direct and slitless observations
  • Build associations
    • Can/should we use the JWST pipeline machinery for such associations?

Geometric transformations

  • Direct image: x,y ↔ RA, DEC

  • Slitless image: 

    • ra, dec, wavelength → x, y

    • x,y | RA, dec →  wavelength

    • x,y | wavelength → ra, dec

Astrometric registration

Simulations 

Contamination

Background Subtraction

Flatfielding

1D Spectral Extraction

2D Spectral Extraction

1D Forward modeling

2D Forward modeling

Combine spectra single PA

Combine spectra multiple orients

Documentation

  •  Getting Started (landing page)
  •  Installation
  •  Overview
  •  Input/Output
    • images
    • spectra
    • catalogs and tables
    • reference files
  •  Matching direct and slitless observations
  •  Astrometric registration
  •  Background subtraction
  •  Spectral extraction
  •  Contamination
  •  Combining spectra
  •  Simulations and forward modeling
  •  Using Astrogrism with other packages
    • specutils
    • grizli
    • pylinear
    • EM2D
    • MIRAGE?
  • Terminology:
    • Use "slitless" instead of grism?




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