A multi-author overview of machine-learning algorithms proposed for instrument modelling, data analysis, simulation and inference in SKA Cosmic Dawn and Epoch of Reionization science.
Mitigating antenna gain errors with HyFoReS in CHIME simulations
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abstract
Hybrid Foreground Residual Subtraction (HyFoReS) is a new family of algorithms designed to remove systematics-induced foreground contamination for 21-cm intensity mapping data. Previously, the algorithm was shown to be effective in mitigating beam perturbations in sky maps from the Canadian Hydrogen Intensity Mapping Experiment (CHIME). In this study, we apply HyFoReS to CHIME simulations and test the algorithm's ability to mitigate antenna gain-type systematics in polarized visibilities. Simulating a two-cylinder telescope similar to the CHIME pathfinder, we find that HyFoReS reduces foreground bias caused by bandpass perturbations to a level below the thermal noise, provided that the RMS value of the perturbations is on the order of $10^{-4}$ or lower. When tested with complex antenna-dependent gain errors, HyFoReS can reduce residual foreground bias in the power spectrum by up to three orders of magnitude. While noise bias and second-order perturbations are currently the limiting factors for the algorithm, we have demonstrated that HyFoReS can suppress gain-induced foreground leakage in polarized data from 21-cm telescopes, aiding in the detection of the 21-cm auto-power spectrum for hydrogen intensity mapping experiments.
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astro-ph.IM 1years
2026 1verdicts
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Machine Learning and the SKA for Cosmic Dawn and the Epoch of Reionization
A multi-author overview of machine-learning algorithms proposed for instrument modelling, data analysis, simulation and inference in SKA Cosmic Dawn and Epoch of Reionization science.