Speaker
Description
One of the foremost challenges in detecting the 21-cm signal from the high-redshift Universe is the astrophysical foreground emission, which obscures the faint background signal. MWA and LOFAR, being similar instruments by design, share broadly similar philosophies for modelling and subtracting known foreground contributions from visibilities, and for estimating the 21-cm signal power spectrum. However, the treatment of unmodelled foregrounds differs between the two experiments. MWA analyses adopt a foreground avoidance strategy, in which the spherical power spectrum is estimated by excluding foreground-contaminated modes. In contrast, LOFAR analyses employ a foreground subtraction approach, where unmodelled foregrounds are statistically separated from the 21-cm signal using Gaussian process regression (GPR) and subsequently removed from the data. This study investigates the applicability of GPR as a foreground subtraction tool for MWA observations, using the software developed by the LOFAR-EoR collaboration over the past decade. We present initial results from this study and demonstrate the opportunities this approach provides, both in reducing foreground contamination in MWA power spectrum upper limits and in understanding the underlying systematics from a different perspective. This work complements the study presented in the talk by Nichole Barry, which explores the converse problem of analysing LOFAR data using MWA software pipelines.
| Timeslot preferences | Morning session would be ideal, since I will be attending the meeting online from Australia (AEST). |
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