It took one hell of a long time, but I felt so probabilistically righteous.
   — J. J. User

The Tractor is a new project (by Lang & Hogg) to generate probabilistically justified astronomical catalogs from multi-pass, multi-band imaging. Instead of simply measuring star and galaxy properties, the Tractor optimizes a likelihood for the catalog properties given the data and an informative noise model. In this sense, it treats the catalog as the parameters of a flexible model—a model with variable complexity. In principle the Tractor can even sample in catalog space and marginalize out uninteresting catalog properties.

Traditionally, sources are detected in astronomical imaging with single-point estimates of significance and then measured and then characterized with single-point estimates of their properties. There are several significant issues with this approach. One is that the single-point estimates are rarely (though sometimes) maximum-likelihood estimates. Another is that the code must make hard decisions about the existence of souces or whether to analyze a source as a star or a galaxy, and these are rarely justified in terms of probabilistic utility. Another is that sources can overlap, and naive single-point estimates cannot account for the mixture of influences on the image pixels. Another is that single-point estimates cannot be combined with non-trivial priors to produce more probable solutions when informative prior or external data exist.

All of these issues can be ameliorated if we see catalog generation or measurement as the optimization of a justified scalar objective function. To be justified, this function must have both probabilistic content (the likelihood or the probability of the data given the model) and utility content (so that models of differing complexity can be compared). The Tractor is a first-in-class implementation of these ideas [ less].