Persistence-based topological descriptors from 21 cm forest spectra provide complementary constraints on X-ray heating efficiency and warm dark matter free-streaming scale.
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Starlet l1-norm applied to simulated HI brightness temperature maps at z~0.4 yields almost 3x higher figure of merit for cosmological parameters than angular power spectrum by capturing non-Gaussian information and shows robustness to observational systematics.
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.
citing papers explorer
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Topological Signatures of Heating and Dark Matter in the 21 cm Forest
Persistence-based topological descriptors from 21 cm forest spectra provide complementary constraints on X-ray heating efficiency and warm dark matter free-streaming scale.
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Beyond power spectrum to unveil systematics on HI intensity maps
Starlet l1-norm applied to simulated HI brightness temperature maps at z~0.4 yields almost 3x higher figure of merit for cosmological parameters than angular power spectrum by capturing non-Gaussian information and shows robustness to observational systematics.
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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.