Multi-band images

[jeff-regier]

Because BLISS is a Bayesian model, it's conceptually straightforward to adapt to the BLISS model to perform inference with multi-band images, but there are some tricky aspects in practice.

Steps:

• to "generate" star/galaxy colors (i.e., to draw colors from the prior), sampling from SDSS catalogs for arbitrary runs, camcols, and fields using the PhotoFullCatalog class
• train encoder with images containing 2 sdss bands: r and g. don’t bother aligning the images or explicitly pairing each band’s background with the image in the band. just pass a four channel image (2 backgrounds, 2 images) to the encoder. verify that two bands leads to better validation accuracy than one with synthetic images
• verify that two bands is better than one with real sdss images. Presumably it will be necessary to train with simulated images whose bands are not pixel aligned
• try augmenting the image data with derived/transformed images: z-score, log transformed, coadds
• train the encoder with all 5 sdss bands, doing what it takes to continue seeing performance gains as more bands are added (e.g., conv3d that pairs each image with itd background)
• write a pytest test case showing multiple band input works, illustrate performance in output, and baseline comparison with colors (ideally a clear improvement over processing just the r band)
• create a Jupyter notebook with the case_studies/multiband directory that contains the results we'd need for a paper about "Simulation-based Multiband Probabilistic Cataloging" (e.g., show that multiband is better than single-band for cataloging SDSS, with the decals catalog serving as ground truth)

[github-actions]

Stale issue message

[ismael-mendoza]

We should still do this for sure, I've talked to several people that are very interested in multi-band detection for instance

[jeff-regier]

closed by PR #835