TL;DR
This paper introduces a fully Bayesian stacking method to accurately measure radio source counts at microJy levels, revealing that normal spiral galaxies dominate these faint populations and highlighting biases in previous approaches.
Contribution
It presents the first fully Bayesian stacking framework for radio source count estimation, addressing confusion bias and applying it to simulations and real data at unprecedented faint flux levels.
Findings
Bayesian stacking accurately recovers counts in simulations.
Observed counts remain flat down to 40 microJy before declining.
Normal spiral galaxies dominate the faint radio source population.
Abstract
Measuring radio source counts is critical for characterizing new extragalactic populations, brings a wealth of science within reach and will inform forecasts for SKA and its pathfinders. Yet there is currently great debate (and few measurements) about the behaviour of the 1.4-GHz counts in the microJy regime. One way to push the counts to these levels is via 'stacking', the covariance of a map with a catalogue at higher resolution and (often) a different wavelength. For the first time, we cast stacking in a fully bayesian framework, applying it to (i) the SKADS simulation and (ii) VLA data stacked at the positions of sources from the VIDEO survey. In the former case, the algorithm recovers the counts correctly when applied to the catalogue, but is biased high when confusion comes into play. This needs to be accounted for in the analysis of data from any relatively-low-resolution SKA…
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