April 7, 2017 Thesis Open Access

Probabilistic Cataloging of the Globular Cluster Messier 2: Improved PSF Photometry of Crowded Stellar Fields

Lee, Benjamin Charles Germain

Thesis supervisor(s)

Finkbeiner, Douglas

Cataloging is an essential part of the data processing pipelines of modern surveys: most astrophysicists conduct research using catalogs of astronomical objects rather than raw telescope images. Though traditional cataloging packages perform well in most instances, crowded fields are particularly challenging due to the blending of and covariance between neighboring sources. With the improved depth of future telescope surveys, the fraction of exposures in the crowded limit will only continue to increase. As a result, it is more important than ever to explore new methods of crowded field photometry. In this thesis, I present the first application of probabilistic cataloging to real optical data. Probabilistic cataloging uses Bayesian inference and a trans-dimensional search to sample the space of all possible catalogs consistent with an image, producing an ensemble of catalogs instead of just one. Unlike catalogs produced by traditional cataloging packages, the resulting catalog ensemble retains fully marginalized deblending uncertainties and covariances between sources.

I quantitatively show that probabilistic cataloging outperforms DAOPHOT, the best-performing of the traditional stellar photometry packages in the crowded limit, on a 100×100 pixel cutout of a Sloan Digital Sky Survey (SDSS) r-band image of the globular cluster Messier 2 (Becker et al. 2007). Adopting a Hubble Space Telescope catalog of the same region of sky as ground truth, I show that the catalog ensemble generated using probabilistic cataloging is complete to over 1 magnitude deeper than the corresponding DAOPHOT catalog while maintaining a similar false discovery rate. Additional tests show that probabilistic cataloging is robust to different seeing conditions. Lastly, I provide a labeling procedure by which the catalog ensemble can be distilled to a single “condensed” catalog with fully marginalized uncertainties that maintains a similar completeness and false discovery rate to those of the catalog ensemble. These results demonstrate the applicability of probabilistic cataloging to future surveys such as the Large Synoptic Survey Telescope.