# Multiband Probabilistic Cataloging: A Joint Fitting Approach to Point Source Detection and Deblending

## TL;DR

This paper extends probabilistic cataloging to multiple bands, significantly improving sensitivity and speed, and demonstrates its effectiveness in simulated and real astronomical data for detecting and deblending sources.

## Contribution

The authors develop a multiband probabilistic cataloging method that enhances detection sensitivity and computational efficiency over previous single-band approaches.

## Key findings

- Achieves 0.4 mag greater sensitivity than single-band PCAT.
- Speeds up processing by 500 times compared to previous methods.
- Outperforms DAOPHOT in depth and false discovery rate on SDSS data.

## Abstract

Probabilistic cataloging (PCAT) outperforms traditional cataloging methods on single-band optical data in crowded fields (Portillo et al. 2017). We extend our work to multiple bands, achieving greater sensitivity ($\sim$ 0.4 mag) and greater speed (500x) compared to previous single-band results. We demonstrate the effectiveness of multiband PCAT on mock data, both in terms of recovering accurate posteriors in the catalog space, and in 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 $\sim0.4$ mag deeper than single-band probabilistic cataloging and has a false discovery rate less than 20\% for F606W$\leq 20$. Compared to DAOPHOT, the two-band SDSS catalog fit goes nearly 1.5 magnitudes deeper using the same data, and maintains a lower false discovery rate down to F606W$\sim 20.5$. 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.