Constraining Reionization with Ly{\alpha} Damping-Wing Absorption in Galaxy Spectra: A Machine Learning Model Based on Reionization Simulations
Hyunbae Park, Intae Jung, Hidenobu Yajima, Jenny Sorce, Paul R., Shapiro, Kyungjin Ahn, Pierre Ocvirk, Romain Teyssier, Gustavo Yepes, Ilian, T. Iliev, and Joseph S. W. Lewis

TL;DR
This paper introduces a machine learning approach using random forest classification to analyze galaxy spectra and constrain the ionization state of the intergalactic medium during reionization, leveraging JWST observations and simulations.
Contribution
It develops a novel methodology combining simulations and machine learning to estimate the IGM ionization fraction from low-resolution galaxy spectra.
Findings
Individual galaxy spectra can tightly constrain the ionization fraction with high SNR.
The method is effective during early reionization when IGM opacity is high.
Applied to JWST data, it places upper bounds on the ionization fraction at various redshifts.
Abstract
Recently, NIRSpec PRISM/CLEAR observations by JWST have begun providing rest-frame UV continuum measurements of galaxies at , revealing signatures of Ly damping-wing (DW) absorption by the intergalactic medium (IGM). We develop a methodology to constrain the global ionization fraction of the IGM using low-resolution spectra, employing the random forest classification (RFC) method. We construct mock spectra using the simulated galaxies and the IGM from the Cosmic Dawn II simulation and train RFC models to estimate at the redshift of the source and to detect the presence of a damped Ly absorber (DLA). We find that individual galaxy spectra with spectral bins between 1220 and 1270 {\AA} and with signal-to-noise ratios greater than 20 can place tight constraints on , provided the UV continuum is accurately modeled. This…
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Taxonomy
TopicsGeophysics and Gravity Measurements · Optical Polarization and Ellipsometry · Adaptive optics and wavefront sensing
