predict spectra using photometry

The SDSS spectra can be thought of as "labels" for objects detected in the imaging, each of which has ugriz photometry and some shape and position parameters. Can we train a model with this enormous amount of data to predict the spectra using the photometry? One thing that says "yes" is that photometric redshifts (for galaxies and quasars), photometric distances (for stars), and photometric temperatures and metallicities (for stars) all work well. One thing that says "no" is that there is far more information (in a technical sense) in the spectra than in the photometry. All this said, it is an absolutely great "Data Science" demonstration project, and it might create some new ideas for LSST-era astrophysics projects. In principle, it will also get us predictions about the spectral types and redshifts of many objects that lack spectra!

forward scattering by dust

Is the Universe transparent? This issue has been interesting me for many years; the answer is "yes" of course, but just how transparent? There are many ways to look at transparency, but a talk by Corrales last week got me thinking about it again: Take some point sources, regress their images against brightness, color, and line-of-sight dust amplitude. Do you see a scattering halo that is correlated with dust? In the optical and UV, I think you won't, based on unpublished work I have done previously. However, there might be a tiny signal. Also, there is more likely to be an effect in the x-ray, which (with Chandra) is accessible. And there is abundant archival data for this project in every waveband from infrared to X-ray.