Fully-Bayesian stacking in the presence of confusion

TL;DR

This paper introduces a fully Bayesian stacking method that accounts for confusion effects, enabling accurate extraction of galaxy counts in deep radio surveys like SKADS and MIGHTEE.

Contribution

It extends stacking analysis to include confusion effects within a Bayesian framework, improving accuracy of galaxy count estimations in radio astronomy.

Findings

Correctly recovers source counts in simulated data with confusion

Provides a novel estimator for confusion impact on stacking analyses

Enhances analysis capabilities for upcoming deep radio surveys

Abstract

Multi-wavelength astronomical studies brings a wealth of science within reach. One way to achieve a cross-wavelength analysis is via `stacking', i.e. combining precise positional information from an image at one wavelength with data from one at another wavelength in order to extract source-flux distributions and other derived quantities. For the first time we extend stacking to include the effects of confusion. We develop our algorithm in a fully Bayesian framework and apply it to the Square Kilometre Array Design Study (SKADS) simulation in order to extract galaxy number counts. Previous studies have shown that recovered source counts are highly biased high when confusion is non-negligible. With this new method, source counts are returned correctly. We also describe a novel estimator for quantifying the impact of confusion on stacking analyses. This method is an essential step in exploiting scientific return for upcoming deep radio surveys, e.g. MIGHTEE on MeerKAT.