Abstract
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.
Subject
Astronomy and Astrophysics