The use of physical optics and machine learning in modelling the QUBIC beam pattern

Abstract

The Q and U Bolometric Interferometer for Cosmology (QUBIC) is a ground-based telescope which will observe thecosmic microwave background (CMB) with the goal of detecting its extremely faint primordial B-mode polarizationpattern as observational evidence for the theory of inflation in the early Universe.QUBIC has a novel bolometric interferometer design that allows it to have precise control over instrument systematics andto remove astronomical foregrounds which would otherwise obscure the CMB polarization signal. Due to itsinterferometric design, QUBIC has a complex multi-peaked antenna beam pattern known as the synthesized beam whichmust be well known in order to correctly process the QUBIC data. We have modelled the QUBIC beam pattern at multiplefrequencies and for detectors located at different positions in the focal plane using precise electromagnetic and physicaloptics simulations. For the complex setup of the QUBIC telescope these simulations are computationally expensive.In this work we use machine learning to interpolate the beam at multiple frequencies and detector locations in order toreduce the amount of computation required to model it. For our purposes, two types of neural networks were used: aMultilayer Perceptron (MLP) model and a Long Short-Term Memory (LSTM) model. For the amount of data generated,only the LSTM model was successful in replicating the synthesized beam to a satisfactory degree. We used the LSTMmachine learning (ML) model to generate the synthesized beam for all 248 detectors at 150 GHz and compared its use inour analysis pipeline to a fully simulated PO beam and an approximate analytical model. From this it can be seen thatalthough the ML prediction did not replicate the PO synthesized beam perfectly, it was closer than the beam produced withthe analytical formula. Finally, we show the effect of the differences in beam pattern prediction on the recovered B-modespectrum of the CMB.