Efficient exploration of reionization parameters for the upcoming 21 cm observations using a photon conserving semi-numerical model SCRIPT

TL;DR

This paper evaluates the semi-numerical code SCRIPT for constraining reionization parameters from 21 cm observations, demonstrating its unbiased and resolution-insensitive performance, and introduces a simple model for efficient global reionization history recovery.

Contribution

It assesses SCRIPT's effectiveness in parameter inference for reionization and proposes a simple template model that balances accuracy with computational efficiency.

Findings

SCRIPT can unbiasedly recover input parameters

Inferences are insensitive to simulation resolution

Simple models can reliably recover global reionization history

Abstract

One of the most promising probes to constrain the reionization history of the universe is the power spectrum of neutral hydrogen 21 cm emission fluctuations. The corresponding analyses require computationally efficient modelling of reionization, usually achieved through semi-numerical simulations. We investigate the capability of one such semi-numerical code, SCRIPT, to constrain the reionization parameters. Our study involves creating a mock data set corresponding to the upcoming SKA-Low, followed by a Bayesian inference method to constrain the model parameters. In particular, we explore in detail whether the inferred parameters are unbiased with respect to the inputs used for the mock, and also if the inferences are insensitive to the resolution of the simulation. We find that the model is successful on both fronts. We also develop a simple template model of reionization which can mimic the complex physical processes like inhomogeneous recombinations and radiative feedback and show that it can recover the global reionization history reliably with moderate computational cost. However, such simple models are not suitable for constraining the properties of the ionizing sources. Our results are relevant for constraining reionization using high-quality data expected in future telescopes.