An emulator for the ionizing photon mean free path in ultra-high resolution simulations: the implications of mean free path measurements for the reionization history
Hurum Maksora Tohfa, Christopher Cain, Matthew McQuinn, Anson D'Aloisio

TL;DR
This paper introduces a deep learning emulator for the ionizing photon mean free path during reionization, enabling efficient interpretation of measurements and constraints on reionization history without large-scale simulations.
Contribution
We develop a neural network-based emulator trained on high-resolution simulations to predict the mean free path, improving accuracy and efficiency in modeling reionization.
Findings
Models favor late reionization with significant neutral fractions at z<6.
Estimated reionization midpoint at z_re=6.8±1.2, consistent with other constraints.
Ionizing emissivity declines by a factor of 2-3 between z=6 and 4.8.
Abstract
Measurements of the mean free path of ionizing photons from high-redshift quasar spectra at - constrain the reionization history, but interpreting them requires modeling the kiloparsec-scale clumping that large-volume reionization simulations cannot resolve. We present a deep learning emulator for the mean free path (MFP) trained on high-resolution cosmological radiative transfer simulations of ionization fronts sweeping through small 2 comoving~Mpc/h volumes. Using a residual multi-layer perceptron neural network, we predict the MFP at a given redshift as a function of the reionization redshift, photoionization rate, wavelength, and box-scale density, achieving a median relative error of 1.6\% across nearly four orders of magnitude in MFP. Integrating its predictions over box-scale overdensity and an extended reionization history allows the emulator to predict the global…
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Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Astronomy and Astrophysical Research · Cosmology and Gravitation Theories
