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.

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

Outputs

• A 1D extracted spectrum in a format that can be used by specutils
  • Flux vs. wavelength
  • Uncertainties
  • Propagated data-quality flags
  • Flags of the pixels that are contaminated by other sources; IDs of the contaminating sources

Computations

• 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
  • As a quick & dirty story, this could be as simple as looking at overlapping bounding boxes
  • Probably best to record the IDs of the contaminating sources

Drawbacks

• 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