PCAT-DE: Reconstructing point-like and diffuse signals in astronomical images using spatial and spectral information
Richard M. Feder, Victoria Butler, Tansu Daylan, Stephen K. N., Portillo, Jack Sayers, Benjamin J. Vaughan, Catalina V. Zamora, Michael, Zemcov

TL;DR
This paper introduces PCAT-DE, a novel probabilistic modeling tool that simultaneously reconstructs point-like and diffuse signals in astronomical images using spatial and spectral information, improving source separation and measurement accuracy.
Contribution
The work extends probabilistic cataloging to incorporate spatial templates and Fourier components, enabling joint modeling of point sources and diffuse emission in complex astronomical data.
Findings
Reliable separation of point sources and diffuse emission demonstrated on synthetic data.
Effective recovery of cosmic infrared background galaxy counts despite contamination.
Accurate measurement of the thermal Sunyaev-Zel'dovich effect even when subdominant.
Abstract
Observational data from astronomical imaging surveys contain information about a variety of source populations and environments, and its complexity will increase substantially as telescopes become more sensitive. Even for existing observations, measuring the correlations between point-like and diffuse emission can be crucial to correctly inferring the properties of any individual component. For this task information is typically lost, either because of conservative data cuts, aggressive filtering or incomplete treatment of contaminated data. We present the code PCAT-DE, an extension of probabilistic cataloging designed to simultaneously model point-like and diffuse signals. This work incorporates both explicit spatial templates and a set of non-parametric Fourier component templates into a forward model of astronomical images, reducing the number of processing steps applied to the…
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Taxonomy
TopicsCalibration and Measurement Techniques · Spectroscopy and Chemometric Analyses · Advanced Statistical Methods and Models
