A GPR-Based Emulator for Semi-numerical Reionization Code SCRIPT: Parameter Inference from 21 cm Data

TL;DR

This paper introduces a Gaussian Process Regression emulator for the semi-numerical reionization code SCRIPT, enabling efficient Bayesian parameter inference from 21 cm data with significantly reduced computational costs.

Contribution

The work presents a novel GPR-based emulator for SCRIPT that accelerates parameter inference, utilizing coarse simulations to identify high-probability regions and reducing analysis time by about tenfold.

Findings

Emulator provides accurate posterior estimates comparable to traditional MCMC.

Training with coarse simulations efficiently identifies high-probability parameter regions.

Overall analysis time is reduced by approximately an order of magnitude.

Abstract

Semi-numerical models of reionization typically involve a large number of unknown parameters whose values are constrained by comparing with observations. Increasingly often, exploring this parameter space using semi-numerical simulations can become computationally intensive, thus necessitating the use of emulators. In this work, we present a likelihood emulator based on Gaussian Process Regression (GPR) for our semi-numerical reionization code, SCRIPT, and use it for parameter inference using mock 21 cm power spectrum data and Bayesian MCMC analysis. A unique aspect of our methodology is the utilization of coarse resolution simulations to identify high-probability regions within the parameter space, employing only a moderate amount of computational time. Samples drawn from these high-probability regions are used to construct the training set for the emulator. The subsequent MCMC using this GPR-trained emulator is found to provide parameter posteriors that agree reasonably well with those obtained using conventional MCMC. The computing time for the analysis, which includes both generation of training sets and training the emulator, is reduced by approximately an order of magnitude. This methodology is particularly advantageous in scenarios where one wants to use different parametrizations of reionization models and/or needs to start with broad prior distributions on the parameters, offering an efficient and effective means of parameter inference.