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

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