摘要：Probabilistic cataloging (PCAT) outperforms traditional cataloging methods on single-band optical data in crowded fields.We extend our work to multiple bands, achieving greater sensitivity (~0.4 mag) and greater speed (500×) compared to previous single-band results.We demonstrate the effectiveness of multiband PCAT on mock data, in terms of both recovering accurate posteriors in the catalog space and directly deblending sources.When applied to Sloan Digital Sky Survey (SDSS) observations of M2, taking Hubble Space Telescope data as truth, our joint fit on r- and i-band data goes ~0.4 mag deeper than single-band probabilistic cataloging and has a false discovery rate less than 20% for F606W ≤ 20.Compared to DAOPHOT, the two-band SDSS catalog fit goes nearly 1.5 mag deeper using the same data and maintains a lower false discovery rate down to F606W ~ 20.5.Given recent improvements in computational speed, multiband PCAT shows promise in application to large-scale surveys and is a plausible framework for joint analysis of multi-instrument observational data.https://github.com/RichardFeder/multiband_pcat.
关键词：Catalogs;Star counts;Sky surveys;Globular star clusters;Bayesian statistics;Hierarchical models
