Machine Learning Aids 21 cm Cosmology Research

This chapter discusses the application of machine learning (ML) in redshifted 21 cm cosmology, focusing on the cosmic dawn, the Epoch of Reionization, and the scientific program of SKA-Low. The 21 cm signal, which directly probes diffuse neutral hydrogen, is a valuable tool for studying these periods. However, its analysis is challenging due to its dependence on density, ionization, heating, radiation backgrounds, and instrumental response.

The chapter reviews the basic physical ingredients needed for understanding the 21 cm signal, including the global signal, spatial fluctuations, morphology-aware summaries, and the 21 cm forest. It also 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.

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 ML is useful due to one-dimensional spectra, small-scale information, and uncertain source populations. The use of ML offers a promising avenue for advancing our understanding of the early universe through 21 cm cosmology.

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