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1. How does grizli specify parameters to vary?
2. What is the merit-function for the fit? 
3. How are the data weighted ?
4. What optimizer is used?
5. How are uncertainties calculated?
   1. Ans: detector noise model (straightforward because we are in FLT frames)
   2. Drawback – can be slow if there are many exposures (e.g. FIGS)
6. Which modules/functions/classes are doing the fitting?

Ans:

• Turns input 1D spectrum & flux into 2D dispersed using object morphology
• LSQ fitting scaling the models (template library)  
• multifit is doing the chisq minimization
• Current approach – generate static contamination model  (first starting with flat spectrum, then polynomials)
• Start with brightest object, subtract fainter ones, fit polynomial, then iterate to fainter objects
• For now, stop there for contamination – could imagine going to the next step of iterating all the fitted spectra
• Possible to simultaneously fit in cutouts with multiple objects 

GrismDisperser

• In model.GrismDisperser, do compute_model and compute_model_psf correspond to pixel- and object-based dispersion, respectively?
  

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