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Simulation-Based Inference using MNRE recovers astrophysical parameters in 21cm cosmology, suggesting accessibility for new researchers.
The 21-cm line of neutral hydrogen is a promising probe of the early Universe, yet extracting astrophysical parameters from its power spectrum remains a major challenge. We present a beginner-friendly PyTorch pipeline for Marginal Neural Ratio Estimation (MNRE), a Simulation-Based Inference (SBI) method that bypasses explicit likelihoods. Using 21cmFAST simulations, we show that MNRE can recover key astrophysical parameters such as the ionizing efficiency $ζ$ and X-ray luminosity LX directly from power spectra. Our implementation prioritizes transparency and accessibility, offering a practical entry point for new researchers in 21-cm cosmology.
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Sen et al. (2025) studied this question.
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