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.

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
  • Goodness-of-fit
    • Overall, and for each source individually

Computations

  • 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

Drawbacks

  • Takes more effort and time than a spectral extraction based on a bounding box.
  • Uncertainties depend more on the input assumptions (e.g. models for contaminating sources) 
  • This approach won't find isolated emission lines.
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