Cosmological constraints from low redshift 21 cm intensity mapping with machine learning

Abstract

ABSTRACT The future 21 cm intensity mapping observations constitute a promising way to trace the matter distribution of the Universe and probe cosmology. Here, we assess its capability for cosmological constraints using as a case study the BINGO radio telescope, that will survey the Universe at low redshifts (0.13 < z < 0.45). We use neural networks (NNs) to map summary statistics, namely, the angular power spectrum (APS) and the Minkowski functionals (MFs), calculated from simulations into cosmological parameters. Our simulations span a wide grid of cosmologies, sampled under the ΛCDM scenario, {Ωc, h}, and under an extension assuming the Chevallier–Polarski–Linder (CPL) parametrization, {Ωc, h, w0, wa}. In general, NNs trained over APS outperform those using MFs, while their combination provides 27 per cent (5 per cent) tighter error ellipse in the Ωc–h plane under the ΛCDM scenario (CPL parametrization) compared to the individual use of the APS. Their combination allows predicting Ωc and h with 4.9 and 1.6 per cent fractional errors, respectively, which increases to 6.4 and 3.7 per cent under CPL parametrization. Although we find large bias on wa estimates, we still predict w0 with 24.3 per cent error. We also confirm our results to be robust to foreground contamination, besides finding the instrumental noise to cause the greater impact on the predictions. Still, our results illustrate the capability of future low-redshift 21 cm observations in providing competitive cosmological constraints using NNs, showing the ease of combining different summary statistics.