English

Demonstration of hybrid foreground removal on CHIME data

Cosmology and Nongalactic Astrophysics 2024-08-20 v1 Instrumentation and Methods for Astrophysics General Relativity and Quantum Cosmology

Abstract

The main challenge of 21 cm cosmology experiments is astrophysical foregrounds which are difficult to separate from the signal due to telescope systematics. An earlier study has shown that foreground residuals induced by antenna gain errors can be estimated and subtracted using the hybrid foreground residual subtraction (HyFoReS) technique which relies on cross-correlating linearly filtered data. In this paper, we apply a similar technique to the CHIME stacking analysis to subtract beam-induced foreground contamination. Using a linear high-pass delay filter for foreground suppression, the CHIME collaboration reported a 11.1σ11.1\sigma detection in the 21 cm signal stacked on eBOSS quasar locations, despite foreground residual contamination mostly due to the instrument chromatic transfer function. We cross-correlate the foreground-dominated data at low delay with the contaminated signal at high delay to estimate residual foregrounds and subtract them from the signal. We find foreground residual subtraction can improve the signal-to-noise ratio of the stacked 21 cm signal by 1020% 10 - 20\% after the delay foreground filter, although some of the improvement can also be achieved with an alternative flagging technique. We have shown that it is possible to use HyFoReS to reduce beam-induced foreground contamination, benefiting the analysis of the HI auto power spectrum with CHIME and enabling the recovery of large scale modes.

Keywords

Cite

@article{arxiv.2408.08949,
  title  = {Demonstration of hybrid foreground removal on CHIME data},
  author = {Haochen Wang and Kiyoshi Masui and Kevin Bandura and Arnab Chakraborty and Matt Dobbs and Simon Foreman and Liam Gray and Mark Halpern and Albin Joseph and Joshua MacEachern and Juan Mena-Parra and Kyle Miller and Laura Newburgh and Sourabh Paul and Alex Reda and Pranav Sanghavi and Seth Siegel and Dallas Wulf},
  journal= {arXiv preprint arXiv:2408.08949},
  year   = {2024}
}