Cross-Identification of Astronomical Objects

Budavari, Tamas

The cross-identification of objects in separate observations is one of the most fundamental problems in astronomy. Most scientific analyses build on combined, multicolor and/or multiepoch datasets, and heavily rely on the quality of their associations. Crossmatching, however, is a hard problem not only computationally but also statistically. We will review a novel probabilistic approach that delivers on both fronts. It yields simple, intuitive formulas in the usual limits that are easily calculable, but also generalizes to more complicated situations. It naturally accommodates more sophisticated physical models, such as that of the spectral energy distribution of galaxies or the proper motion of stars, and can be extended to resolved sources and transients. Building on this new mathematical framework, new tools are being developed to enable automated associations.

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