kNN CDF statistics detect 21cm-galaxy cross-correlations more effectively than two-point methods and distinguish reionization models at fixed ionized fraction even with noise and foregrounds.
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A Gompertzian reionization model with three nuisance parameters demotes optical depth to a derived quantity, reducing its uncertainty by a factor of three and revealing potential neutrino mass tension in CMB analyses.
Large reionization simulations show that the distribution of dark gaps in the Ly-α forest favors models with reionization completing at z≈5.4 over earlier or constant short mean-free-path scenarios.
Simulations show hybrid foreground mitigation (GPR + PCA combined with avoidance) recovers the HI 21cm signal within 2σ for gain calibration errors ≤1% in SKA1-Low AA* observations over 0.05-0.5 Mpc^{-1} scales.
Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.
citing papers explorer
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Nearest Neighbour-Based Statistics for 21cm-Galaxy Cross-Correlations in the Epoch of Reionization
kNN CDF statistics detect 21cm-galaxy cross-correlations more effectively than two-point methods and distinguish reionization models at fixed ionized fraction even with noise and foregrounds.
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Into the Gompverse: A robust Gompertzian reionization model for CMB analyses
A Gompertzian reionization model with three nuisance parameters demotes optical depth to a derived quantity, reducing its uncertainty by a factor of three and revealing potential neutrino mass tension in CMB analyses.
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Studying dark gaps in Ly-$\alpha$ forest transmission with large reionization simulations
Large reionization simulations show that the distribution of dark gaps in the Ly-α forest favors models with reionization completing at z≈5.4 over earlier or constant short mean-free-path scenarios.
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Mitigating gain calibration errors from EoR observations with SKA1-Low AA*
Simulations show hybrid foreground mitigation (GPR + PCA combined with avoidance) recovers the HI 21cm signal within 2σ for gain calibration errors ≤1% in SKA1-Low AA* observations over 0.05-0.5 Mpc^{-1} scales.
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Machine Learning Techniques for Astrophysics and Cosmology: Lyman-$\alpha$ forest
Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.