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

Novaes, Camila P.; de Mericia, Eduardo J.; Abdalla, Filipe B.; Wuensche, Carlos A.; Santos, Larissa; Delabrouille, Jacques; Remazeilles, Mathieu; Liccardo, Vincenzo

doi:10.1093/mnras/stad2932

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

Camila P. Novaes
(
- Sao Jose, INPE
)
,
Eduardo J. de Mericia
(
- Sao Jose, INPE
)
,
Filipe B. Abdalla
(
- Sao Jose, INPE and
- U. Coll. London and
- U. Sao Paulo (main) and
- Rhodes U.
)
,
Carlos A. Wuensche
(
- Sao Jose, INPE
)
,
Larissa Santos
(
- Yangzhou U.
)

Sep 14, 2023

17 pages

Published in:

Mon.Not.Roy.Astron.Soc. 528 (2024) 2, 2078-2094

Published: 2023

e-Print:

2309.07868 [astro-ph.CO]

DOI:

10.1093/mnras/stad2932 (publication)

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Abstract: (arXiv)

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

\Lambda

CDM scenario, {

\Omega_c, h

}, and under an extension assuming the Chevallier-Polarski-Linder (CPL) parameterization, {

\Omega_c, h, w_0, w_a

}. In general, NNs trained over APS outperform those using MFs, while their combination provides 27% (5%) tighter error ellipse in the

\Omega_c-h

plane under the

\Lambda

CDM scenario (CPL parameterization) compared to the individual use of the APS. Their combination allows predicting

\Omega_c

and

h

with 4.9% and 1.6% fractional errors, respectively, which increases to 6.4% and 3.7% under CPL parameterization. Although we find large bias on

w_a

estimates, we still predict

w_0

with 24.3% 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.

Note:

17 pages, 13 figures

References(0)

Figures(20)

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