Percent-level timing of reionization: self-consistent, implicit-likelihood inference from XQR-30+ Ly$\alpha$ forest data
Yuxiang Qin, Andrei Mesinger, David Prelogovi\'c, George Becker,, Manuela Bischetti, Sarah E. I. Bosman, Frederick B. Davies, Valentina, D'Odorico, Prakash Gaikwad, Martin G. Haehnelt, Laura Keating, Samuel Lai,, Emma Ryan-Weber, Sindhu Satyavolu, Fabian Walter, Yongda Zhu

TL;DR
This paper introduces a Bayesian framework for analyzing Lyman alpha forest data to precisely constrain the timing and properties of the Epoch of Reionization, avoiding rapid ionizing emissivity drops and linking galaxy properties with IGM evolution.
Contribution
It presents a self-consistent, forward-modeling Bayesian approach that directly connects galaxy properties with IGM evolution, providing percent-level constraints on reionization timing from high-quality quasar spectra.
Findings
Reionization ends at z=5.44±0.02
Mid-point of EoR at z=7.7±0.1
Half of ionizing photons come from faint galaxies below detection limits
Abstract
The Lyman alpha (Lya) forest in the spectra of z>5 quasars provides a powerful probe of the late stages of the Epoch of Reionization (EoR). With the recent advent of exquisite datasets such as XQR-30, many models have struggled to reproduce the observed large-scale fluctuations in the Lya opacity. Here we introduce a Bayesian analysis framework that forward-models large-scale lightcones of IGM properties, and accounts for unresolved sub-structure in the Lya opacity by calibrating to higher-resolution hydrodynamic simulations. Our models directly connect physically-intuitive galaxy properties with the corresponding IGM evolution, without having to tune "effective" parameters or calibrate out the mean transmission. The forest data, in combination with UV luminosity functions and the CMB optical depth, are able to constrain global IGM properties at percent level precision in our fiducial…
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Taxonomy
TopicsGene expression and cancer classification
