2025A&A...703A.222C -
Astronomy and Astrophysics, volume 703A222 (2025/11-1)
Hydrogen intensity mapping with MeerKAT: Preserving cosmological signal by optimising contaminant separation.
CARUCCI I.P., BERNAL J.L., CUNNINGTON S., SANTOS M.G., WANG J., FONSECA J., GRAINGE K., IRFAN M.O., LI Y., POURTSIDOU A., SPINELLI M. and WOLZ L.
Abstract (from CDS):
Removing contaminants is a delicate, yet crucial step in neutral hydrogen (H I) intensity mapping and often considered the technique's greatest challenge. Here, we address this challenge by analysing H I intensity maps of about 100 deg2 at redshift z ≈ 0.4 collected by the MeerKAT radio telescope, an SKA Observatory (SKAO) precursor, with a combined 10.5-hour observation. Using unsupervised statistical methods, we removed the contaminating foreground emission and systematically tested, step-by-step, some common pre-processing choices to facilitate the cleaning process. We also introduced and tested a novel multiscale approach: the data were redundantly decomposed into subsets referring to different spatial scales (large and small), where the cleaning procedure was performed independently. We confirm the detection of the H I cosmological signal in cross-correlation with an ancillary galactic data set, without the need to correct for signal loss. In the best set-up we achieved, we were able to constrain the H I distribution through the combination of its cosmic abundance (Ω_H I_) and linear clustering bias (b_H I_) up to a cross-correlation coefficient (r). We measured Ω_H I_b_H I_r = [0.93 ± 0.17] × 10–3 with a ≈6σ confidence, which is independent of scale cuts at both edges of the probed scale range (0.04 ≲ k ≲ 0.3 h Mpc–1), corroborating its robustness. Our new pipeline has successfully found an optimal compromise in separating contaminants without incurring a catastrophic signal loss. This development instills an added degree of confidence in the outstanding science we can deliver with MeerKAT on the path towards H I intensity mapping surveys with the full SKAO.
Abstract Copyright:
© The Authors 2025
Journal keyword(s):
methods: data analysis - methods: statistical - cosmology: observations - large-scale structure of Universe
Simbad objects:
3
Full paper
View the references in ADS
To bookmark this query, right click on this link: simbad:2025A&A...703A.222C and select 'bookmark this link' or equivalent in the popup menu