Joint Multiband Photometry with crowdsource

TL;DR

This paper introduces a multiband extension to the crowdsource photometric pipeline, enabling simultaneous fitting across multiple bands in crowded fields, improving flux consistency and astrometric stability.

Contribution

It presents a novel multiband fitting framework integrated into crowdsource, enhancing photometric accuracy in crowded field imaging.

Findings

Improved flux consistency across bands.

Reduced band-to-band positional scatter.

Enhanced photometric agreement and astrometric stability.

Abstract

We present a new multiband extension to the crowdsource photometric pipeline, enabling simultaneous fitting across multiple imaging bands in crowded fields. The core idea is that multiple images of the same part of the sky should have the same sources at the same locations; only the fluxes in the different images should be allowed to vary in fitting. The framework also allows us to use all images of a given region to detect faint sources, with configurable weighting among the different bandpasses as appropriate for different source spectra. Similar concepts are already present in other crowded field packages like DAOPHOT and DOLPHOT; we now include it in the crowdsource fitting approach. We describe the mathematical formulation of the multiband fit and demonstrate its performance using the Wide-field Infrared Survey Explorer (WISE) W1 and W2 imaging as a concrete application. The multiband algorithm improves flux consistency and reduces band-to-band positional scatter relative to independent-band fitting. We test the method on unWISE coadded tiles spanning both sparse and crowded regions and quantify improvements in photometric agreement and astrometric stability. This framework provides a general foundation for future multiband crowded-field catalogs.