2024 workshop program
A Machine Learning Upgrade to GPR for EoR 21-cm signal extraction from LOFAR data
The work
Abstract
Within the LOFAR Epoch of Reionization (EoR) Key Science Project team, Gaussian Process Regression (GPR) has been used for foreground subtraction from data, to constrain the Neutral Hydrogen 21-cm signal power spectrum from the EoR. To avoid signal loss due to the misestimation of the covariance kernel for the 21-cm signal, we developed a Machine Learning (ML) trained model using a Variational Auto-Encoder. We trained on a large variety of N-body + 1D radiative transfer simulations (GRIZZLY) and tested its limitations by exploring a variety of mock datasets. Further, this setup was applied to the mock data for the Science Data Challenge 3a of SKA by the DOTSS-21 team finishing with one of the highest scores. Now I will show the results of implementing it on 10 nights of observational data from the LOFAR telescope at z~9.1. If possible, I will also discuss its implementation for multi-redshift analyses. Additionally, I will discuss avenues of improving and expanding the training sets used, by developing new simulations.