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Cosmic Dawn (CD) and Epoch of Reionization (EoR) are epochs of the Universewhich host invaluable information about the cosmology and astrophysics of X-rayheating and hydrogen reionization. Radio interferometric observations of the21-cm line at high redshifts have the potential to revolutionize ourunderstanding of the universe during this time. However, modeling the evolutionof these epochs is particularly challenging due to the complex interplay ofmany physical processes. This makes it difficult to perform the conventionalstatistical analysis using the likelihood-based Markov-Chain Monte Carlo (MCMC)methods, which scales poorly with the dimensionality of the parameter space. Inthis paper, we show how the Simulation-Based Inference (SBI) through MarginalNeural Ratio Estimation (MNRE) provides a step towards evading these issues. Weuse 21cmFAST to model the 21-cm power spectrum during CD-EoR with asix-dimensional parameter space. With the expected thermal noise from theSquare Kilometre Array (SKA), we are able to accurately recover the posteriordistribution for the parameters of our model at a significantly lowercomputational cost than the conventional likelihood-based methods. We furthershow how the same training dataset can be utilized to investigate thesensitivity of the model parameters over different redshifts. Our resultssupport that such efficient and scalable inference techniques enable us tosignificantly extend the modeling complexity beyond what is currentlyachievable with conventional MCMC methods.

Constraining the X-ray heating and reionization using 21-cm power spectra with Marginal Neural Ratio Estimation