Constraining below-threshold radio source counts with machine learning
Elisa Todarello,
Andre Scaffidi,
Marco Regis
et al.
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 bel… Show more
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