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We propose a machine-learning-based technique to determine the number densityof radio sources as a function of their flux density, for use innext-generation radio surveys. The method uses a convolutional neural networktrained on simulations of the radio sky to predict the number of sources inseveral flux bins. To train the network, we adopt a supervised approach whereinwe simulate training data stemming from a large domain of possible number countmodels going down to fluxes a factor of 100 below the threshold for sourcedetection. We test the model reconstruction capabilities as well as benchmarkthe expected uncertainties in the model predictions, observing good performancefor fluxes down to a factor of ten below the threshold. This work demonstratesthat the capabilities of simple deep learning models for radio astronomy can beuseful tools for future surveys.

Constraining Below-threshold Radio Source Counts With Machine Learning