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 ∼180 μJy. 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.
@article{arxiv.2511.22574,
title = {ALMACAL. XV. Band 3 ALMA Survey and Number Counts},
author = {Matteo Bonato and Ivano Baronchelli and Gianfranco De Zotti and Leonardo Trobbiani and Michele Delli Veneri and Fabrizia Guglielmetti and Rosita Paladino and Viviana Casasola and Martin Zwann and Marcella Massardi and Elisabetta Liuzzo and Vincenzo Galluzzi and Erlis Ruli},
journal= {arXiv preprint arXiv:2511.22574},
year = {2025}
}