Counting Stars: Developing Probabilistic Cataloguing for Crowded Fields

The depth of next generation surveys poses a great data analysis challenge: these surveys will suffer from crowding, making their images difficult to deblend and catalogue. Sources in crowded fields are extremely covariant with their neighbours and blending makes even the number of sources ambiguous. Probabilistic cataloguing returns an ensemble of catalogues inferred from the image and can address these difficulties. We present the first optical probabilistic catalogue, cataloguing a crowded Sloan Digital Sky Survey r band image cutout from Messier 2. By comparing to a DAOPHOT catalogue of the same image and a Hubble Space Telescope catalogue of the same region, we show that our catalogue ensemble goes more than a magnitude deeper than DAOPHOT. We also present an algorithm for reducing this catalogue ensemble to a condensed catalogue that is similar to a traditional catalogue, except it explicitly marginalizes over source-source covariances and nuisance parameters. We also detail efforts to make probabilistic cataloguing more computationally efficient and extend it beyond point sources to extended objects. Probabilistic cataloguing takes significant computational resources, but its performance compared to existing software in crowded fields make it a enticing method to pursue further.