Constraining Below-threshold Radio Source Counts With Machine Learning

TL;DR

This paper introduces a machine learning method using convolutional neural networks to estimate radio source counts below detection thresholds, aiding future radio survey analyses.

Contribution

It presents a supervised deep learning approach trained on simulated data to accurately predict radio source counts at flux levels below current detection limits.

Findings

Good performance down to ten times below the detection threshold

Effective benchmarking of uncertainties in source count predictions

Demonstrates deep learning's utility for radio astronomy surveys

Abstract

We propose a machine-learning-based technique to determine the number density of radio sources as a function of their flux density, for use in next-generation radio surveys. The method uses a convolutional neural network trained on simulations of the radio sky to predict the number of sources in several flux bins. To train the network, we adopt a supervised approach wherein we simulate training data stemming from a large domain of possible number count models going down to fluxes a factor of 100 below the threshold for source detection. We test the model reconstruction capabilities as well as benchmark the expected uncertainties in the model predictions, observing good performance for fluxes down to a factor of ten below the threshold. This work demonstrates that the capabilities of simple deep learning models for radio astronomy can be useful tools for future surveys.