Page History
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- 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?
- Somehow?
- Accumulates some kind of contamination flag at each wavelength
Why would I not want to do this?
- I might be impatient (see first story), or only interested in a specific feature.
- I might instead be searching for isolated emission lines
Epics
Identify and match data sets
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