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Efficient exploration of parameter spaces is crucial to extract physicalinformation about the Epoch of Reionization from various observational probes.To this end, we propose a fast technique based on Gaussian Process Regression(GPR) training applied to a semi-numerical photon-conserving reionizationmodel, SCRIPT. Our approach takes advantage of the numerical convergenceproperties of SCRIPT and constructs a training set based on low-cost,coarse-resolution simulations. A likelihood emulator is then trained using thisset to produce results in approximately two orders of magnitude lesscomputational time than a full MCMC run, while still generating reasonable 68%and 95% confidence contours. Furthermore, we conduct a forecasting study usingsimulated data to demonstrate the applicability of this technique. This methodis particularly useful when full MCMC analysis is not feasible due to expensivelikelihood computations.

A fast method of reionization parameter space exploration using GPR trained SCRIPT