A fast method of reionization parameter space exploration using GPR trained SCRIPT
Barun Maity, Aseem Paranjape, Tirthankar Roy Choudhury · Monthly Notices of the Royal Astronomical Society · 2023
ABSTRACT Efficient exploration of parameter spaces is crucial to extract physical information about the Epoch of Reionization from various observational probes. To this end, we propose a fast technique based on Gaussian process regression training applied to a semi-numerical photon-conserving reionization model, SCRIPT. Our approach takes advantage of the numerical convergence properties of SCRIPT and constructs a training set based on low-cost, coarse-resolution simulations. A likelihood emulator is then trained using this set to produce results in approximately two orders of magnitude less computational time than a full Markov Chain Monte Carlo (MCMC) run, while still generating reasonable 68 per cent and 95 per cent confidence contours. Furthermore, we conduct a forecasting study using simulated data to demonstrate the applicability of this technique. This method is particularly useful when full MCMC analysis is not feasible due to expensive likelihood computations.