BibTeX

@article{2509.06834v3,
Author        = {Bisweswar Sen},
Title         = {Unlocking 21cm Cosmology with SBI: A Beginner friendly NRE for Inference of Astrophysical Parameters},
Eprint        = {2509.06834v3},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {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 $L_X$ directly from power spectra. Our implementation prioritizes transparency and accessibility, offering a practical entry point for new researchers in 21-cm cosmology.},
Year          = {2025},
Month         = {Sep},
Url           = {http://arxiv.org/abs/2509.06834v3},
File          = {2509.06834v3.pdf}
}