Small-scale power spectrum boosts alter ionization morphology enough that 21 cm power spectra and bubble sizes remain distinguishable from Lambda CDM under current constraints, offering SKA a probe for such deviations.
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A review chapter groups machine learning methods for 21 cm cosmology by their pipeline roles in handling contaminated data, accelerating simulations, and inferring astrophysical parameters.
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Probing power spectrum enhancement at small scales with the SKA
Small-scale power spectrum boosts alter ionization morphology enough that 21 cm power spectra and bubble sizes remain distinguishable from Lambda CDM under current constraints, offering SKA a probe for such deviations.
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Application of Machine Learning to 21 cm Cosmology
A review chapter groups machine learning methods for 21 cm cosmology by their pipeline roles in handling contaminated data, accelerating simulations, and inferring astrophysical parameters.