ALMACAL. XV. Band 3 ALMA Survey and Number Counts

TL;DR

This study uses ALMA calibrator-centered maps to detect faint extragalactic sources, extending radio AGN counts and refining models of galaxy evolution through a novel machine learning approach.

Contribution

Introduces a new machine learning method to analyze ALMA maps, extending source counts by 1.5 orders of magnitude and improving constraints on galaxy populations.

Findings

Extended radio AGN counts by ~1.5 orders of magnitude.

Refined constraints on dusty star-forming galaxies.

Results agree with theoretical models.

Abstract

The ALMACAL project leverages ALMA maps of calibrator-centered fields to conduct deep mm/sub-mm surveys, enabling the detection of extragalactic sources with flux densities orders of magnitude fainter than achievable with other instruments. These faint sources are critical for refining evolutionary models, as their number counts provide key constraints. In this study, we analyzed band-3 ALMACAL maps from 606 calibrator fields, employing a novel machine learning approach to mitigate the often-overlooked bias introduced by the calibrator itself. Supported by extensive simulations, we extended 100 GHz radio AGN counts by approximately 1.5 orders of magnitude in flux density and refined constraints on dusty star-forming galaxies, reaching sensitivities as low as $\sim$180 $\mu$Jy. We have improved the sampling, compared to previous results, in the region of the dominant population transition (between dusty star-forming galaxies and radio AGN). Our results are in good agreement with model predictions.