Application of Machine Learning to 21 cm Cosmology

TL;DR

This paper reviews how machine learning techniques are applied to analyze the complex 21 cm cosmology signals, addressing challenges like foreground contamination and modeling costs, especially for SKA-Low observations.

Contribution

It categorizes ML applications in 21 cm cosmology analysis, highlighting their roles in data processing, modeling acceleration, and inference, with a focus on the 21 cm forest.

Findings

ML methods improve data analysis in contaminated environments

ML accelerates and compresses forward modeling processes

ML enhances inference of astrophysical and cosmological parameters

Abstract

In this chapter, the use of machine learning (ML) in redshifted 21 cm cosmology is discussed, especially for the cosmic dawn, the Epoch of Reionization, and the scientific program of SKA-Low. The 21 cm signal is useful because it can directly probe diffuse neutral hydrogen. At the same time, it is a difficult signal, since the observable depends on density, ionization, heating, radiation backgrounds, and instrumental response in a nonlinear way. The first part of this chapter reviews the basic physical ingredients needed for the later discussion, including the global signal, spatial fluctuations, morphology-aware summaries, and the 21 cm forest. The next part describes the main difficulties for realistic analysis, such as bright foregrounds, radio-frequency interference, ionospheric and calibration effects, incomplete sampling, and the cost of forward modeling in large parameter spaces. Based on this background, ML applications are grouped by their role in the analysis pipeline. Observation-domain methods work on contaminated data products; theory-domain methods accelerate or compress forward modeling; and inference-domain methods connect observables to astrophysical and cosmological constraints. The 21 cm forest is also discussed as a case where one-dimensional spectra, small-scale information, and uncertain source populations make ML useful.