Towards a non-Gaussian Generative Model of large-scale Reionization Maps

TL;DR

This paper introduces a non-Gaussian generative model for large-scale reionization maps that efficiently captures complex structures using Wavelet Phase Harmonics, enabling better statistical analysis of cosmic reionization data.

Contribution

The paper presents a novel non-Gaussian generative model based on summary statistics, particularly Wavelet Phase Harmonics, for simulating diverse reionization maps from limited data.

Findings

WPH-based model effectively reproduces bubble size statistics.

WPH captures most information in non-linear ionization fields.

Model generates diverse maps from a single summary statistic realization.

Abstract

High-dimensional data sets are expected from the next generation of large-scale surveys. These data sets will carry a wealth of information about the early stages of galaxy formation and cosmic reionization. Extracting the maximum amount of information from the these data sets remains a key challenge. Current simulations of cosmic reionization are computationally too expensive to provide enough realizations to enable testing different statistical methods, such as parameter inference. We present a non-Gaussian generative model of reionization maps that is based solely on their summary statistics. We reconstruct large-scale ionization fields (bubble spatial distributions) directly from their power spectra (PS) and Wavelet Phase Harmonics (WPH) coefficients. Using WPH, we show that our model is efficient in generating diverse new examples of large-scale ionization maps from a single realization of a summary statistic. We compare our model with the target ionization maps using the bubble size statistics, and largely find a good agreement. As compared to PS, our results show that WPH provide optimal summary statistics that capture most of information out of a highly non-linear ionization fields.