Foreground simulations for the LOFAR - Epoch of Reionization Experiment
Vibor Jelic, Saleem Zaroubi, Panagiotis Labropoulos, Rajat M. Thomas,, Gianni Bernardi, Michiel A. Brentjens, Ger de Bruyn, Benedetta Ciardi,, Geraint Harker, Leon V.E. Koopmans, Vishambhar N. Pandey, Joop Schaye, Sarod, Yatawatta

TL;DR
This paper presents simulations of astrophysical foregrounds for the LOFAR Epoch of Reionization experiment, demonstrating that polynomial fitting can effectively reconstruct the cosmological 21-cm signal amidst foreground contamination.
Contribution
It introduces simulated foreground datacubes tailored for LOFAR EoR observations and evaluates the effectiveness of polynomial fitting in signal recovery under realistic noise and instrument conditions.
Findings
Polynomial fitting recovers the 21-cm signal with ~52 mK noise levels.
Foreground simulations include polarized galactic synchrotron maps with Faraday effects.
Simple polynomial models are effective under idealized instrument response assumptions.
Abstract
Future high redshift 21-cm experiments will suffer from a high degree of contamination, due both to astrophysical foregrounds and to non-astrophysical and instrumental effects. In order to reliably extract the cosmological signal from the observed data, it is essential to understand very well all data components and their influence on the extracted signal. Here we present simulated astrophysical foregrounds datacubes and discuss their possible statistical effects on the data. The foreground maps are produced assuming 5 deg x 5 deg windows that match those expected to be observed by the LOFAR Epoch-of-Reionization (EoR) key science project. We show that with the expected LOFAR-EoR sky and receiver noise levels, which amount to ~52 mK at 150 MHz after 300 hours of total observing time, a simple polynomial fit allows a statistical reconstruction of the signal. We also show that the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
