ALMA Band 3 Source Counts: A Machine Learning Approach to Contamination Mitigation below 5 Sigma

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Abstract
We performed differential number counts down to 4.25 sigma using ALMA Band 3 calibrator images, which are known for their high dynamic range and susceptibility to various types of contamination. Estimating the fraction of contaminants is an intricate process due to correlated non-Gaussian noise, and it is often compounded by the presence of false positives generated during the cleaning phase. In addition, calibrator extensions further complicate the counting of background sources. In order to address these challenges, our strategy employs a machine learning-based approach utilizing the UMLAUT algorithm. UMLAUT assigns a value to each detection, and it considers how likely it is for there to be a genuine background source or a contaminant. With respect to this goal, we provide UMLAUT with eight observational input parameters, each automatically weighted using a gradient descent method. Our methodology significantly improves the precision of differential number counts, thus surpassing conventional techniques, including visual inspection. This study contributes to a better understanding of radio sources, particularly in the challenging sub-5 sigma regime, within the complex context of a high dynamic range of ALMA calibrator images.

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