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Constraining the polarisation properties of extragalactic point sources is arelevant task not only because they are one of the main contaminants forprimordial cosmic microwave background B-mode detection if the tensor-to-scalarratio is lower than r = 0.001, but also for a better understanding of theproperties of radio-loud active galactic nuclei. We develop and train a machinelearning model based on a convolutional neural network to learn how to estimatethe polarisation flux density and angle of point sources embedded in cosmicmicrowave background images knowing only their positions. To train the neuralnetwork, we use realistic simulations of patches of area 32x32 pixels at the217 GHz Planck channel with injected point sources at their centres. Thepatches also contain a realistic background composed by dust, the CMB andinstrumental noise. Firstly, we study the comparison between true and estimatedpolarisation flux densities for P, Q and U. Secondly, we analyse the comparisonbetween true and estimated polarisation angles. Finally, we study theperformance of our model with real data and we compare our results against thePCCS2. We obtain that our model is reliable to constrain the polarisation fluxabove 80 mJy. For this limit, we obtain errors lower than 30%. Training thesame network with Q and U, the reliability limit is above +-250 mJy fordetermining the polarisation angle of both Q and U sources with a 1sigmauncertainty of +-29deg and +-32deg for Q and U sources respectively. We obtainsimilar results to the PCCS2 for some sources, although we also finddiscrepancies in the 300-400 mJy flux density range with respect to the Planckcatalogue. Based on these results, our model seems to be a promising tool togive estimations of the polarisation flux densities and angles of point sourcesabove 80 mJy in any catalogue with practically null computational time.

Constraining the polarisation flux density and angle of point sources by training a convolutional neural network