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Abstract
ABSTRACT Cosmic Dawn (CD) and Epoch of Reionization (EoR) are epochs of the Universe which host invaluable information about the cosmology and astrophysics of X-ray heating and hydrogen reionization. Radio interferometric observations of the 21-cm line at high redshifts have the potential to revolutionize our understanding of the Universe during this time. However, modelling the evolution of these epochs is particularly challenging due to the complex interplay of many physical processes. This makes it difficult to perform the conventional statistical analysis using the likelihood-based Markov-Chain Monte Carlo (mcmc) methods, which scales poorly with the dimensionality of the parameter space. In this paper, we show how the Simulation-Based Inference through Marginal Neural Ratio Estimation (mnre) provides a step towards evading these issues. We use 21cmFAST to model the 21-cm power spectrum during CD–EoR with a six-dimensional parameter space. With the expected thermal noise from the Square Kilometre Array, we are able to accurately recover the posterior distribution for the parameters of our model at a significantly lower computational cost than the conventional likelihood-based methods. We further show how the same training data set can be utilized to investigate the sensitivity of the model parameters over different redshifts. Our results support that such efficient and scalable inference techniques enable us to significantly extend the modelling complexity beyond what is currently achievable with conventional mcmc methods.
Highlights
The Cosmic Dawn (CD) marks the formation of the first sources of light, which produced high-energy X-ray and UV radiation
1 + nrec where fesc is the fraction of ionizing photons that escape into the intergalactic medium (IGM), f⋆ is the fraction of galactic gas in stars, Nγ/b is the number of ionizing photons produced per baryon in stars and nrec is the average number of times a hydrogen atom recombines
We used 21cmFAST to model the 21-cm power spectra during CD-Epoch of Reionization (EoR) with a six-dimensional astrophysical parameter space. We showed that this framework is significantly more efficient as it directly learns the marginal posteriors of interest through neural networks than the conventional likelihood-based methods such as Markov-Chain Monte Carlo (MCMC), which samples the full joint posterior
Summary
The Cosmic Dawn (CD) marks the formation of the first sources of light, which produced high-energy X-ray and UV radiation. Modeling the 21-cm signal from CD-EoR using full radiative transfer simulations (Mellema et al 2006; Ghara et al 2015) is computationally expensive and unfeasible to perform parameter inference. When the likelihood itself is intractable, techniques such as the Approximate Bayesian Computation (ABC) (Toni et al 2008) can be used to sample from the approximate posterior This approach uses simulated datasets to avoid the likelihood evaluations; it requires the introduction of summary statistics, which can significantly affect the quality of the approximation. These issues can be resolved by performing a SimulationBased Inference (SBI) (Cranmer et al 2020; Papamakarios et al 2019; Alsing et al 2019), where deep learning algorithms along with the ABC are used to estimate the posterior distribution.
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