  Cataloging is challenging in crowded fields because sources are extremely
covariant with their neighbors and blending makes even the number of sources
ambiguous. We present the first optical probabilistic catalog, cataloging a
crowded (~0.1 sources per pixel brighter than 22nd magnitude in F606W) Sloan
Digital Sky Survey r band image from M2. Probabilistic cataloging returns an
ensemble of catalogs inferred from the image and thus can capture source-source
covariance and deblending ambiguities. By comparing to a traditional catalog of
the same image and a Hubble Space Telescope catalog of the same region, we show
that our catalog ensemble better recovers sources from the image. It goes more
than a magnitude deeper than the traditional catalog while having a lower false
discovery rate brighter than 20th magnitude. We also present an algorithm for
reducing this catalog ensemble to a condensed catalog that is similar to a
traditional catalog, except it explicitly marginalizes over source-source
covariances and nuisance parameters. We show that this condensed catalog has a
similar completeness and false discovery rate to the catalog ensemble. Future
telescopes will be more sensitive, and thus more of their images will be
crowded. Probabilistic cataloging performs better than existing software in
crowded fields and so should be considered when creating photometric pipelines
in the Large Synoptic Space Telescope era.
