Article Dans Une Revue The Astrophysical Journal Année : 2025

Constraining Reionization with Lyα Damping-wing Absorption in Galaxy Spectra: A Machine Learning Model Based on Reionization Simulations

Résumé

Recently, NIRSpec PRISM/CLEAR observations by JWST have begun providing rest-frame UV continuum measurements of galaxies at z  7, revealing signatures of Lyα damping-wing (DW) absorption by the intergalactic medium (IGM). We develop a methodology to constrain the global ionization fraction of the IGM (Q HII ) 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 Q HII at the redshift of the source and to detect the presence of a damped Lyα absorber. We find that individual galaxy spectra with spectral bins between 1220 and 1270 Å and with signal-to-noise ratios (SNR) >20 can place tight constraints on Q HII , provided the UV continuum is accurately modeled. This method is particularly effective for the early phase of reionization (Q HII < 50%), when the IGM opacity is high in the DW. As a demonstration, we apply our model to existing NIRSpec PRISM/CLEAR spectra, placing upper bounds of Q HII = 59.6%, 5.6%, and 18.5% at z = 7.7, 9.4, and 10.6, respectively, with 68% confidence, though several modeling uncertainties remain to be discussed. These constraints favor late-starting reionization models, where 80% of the IGM is ionized after z = 8. We conclude that high-SNR observations of carefully selected targets around z ∼ 7-9 can effectively constrain reionization models.

Unified Astronomy Thesaurus concepts: Reionization (1383); Early universe (435); Origin of the universe (1186); Random Forests (1935); Radiative transfer simulations (1967)

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insu-05038221 , version 1 (17-04-2025)

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Hyunbae Park, Intae Jung, Hidenobu Yajima, Jenny G Sorce, Paul R Shapiro, et al.. Constraining Reionization with Lyα Damping-wing Absorption in Galaxy Spectra: A Machine Learning Model Based on Reionization Simulations. The Astrophysical Journal, 2025, 983, ⟨10.3847/1538-4357/adc001⟩. ⟨insu-05038221⟩
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