Frizzle: Combining spectra or images by forward modeling
Frizzle: Combining spectra or images by forward modeling David W. Hogg (NYU), Andrew R. Casey (Monash) arXiv:2403.11011v1 Announce Type: new Abstract: When there are many observations of an astronomical source – many images with different dithers, or many spectra taken at different barycentric velocities – it is standard practice to shift and stack the data, to (for example) make a high signal-to-noise average image or mean spectrum. Bound-saturating measurements are made by manipulating a likelihood function, where the data are treated as fixed, and model parameters are modified to fit the data. Traditional shifting and stacking of data can be converted into a model-fitting procedure,Read More →