BibTeX

@article{2511.02808v1,
Author        = {Diego González-Hernández and Molly Wolfson and Joseph F. Hennawi},
Title         = {Reliable Parameter Inference for the Epoch of Reionization using
  Balanced Neural Ratio Estimation},
Eprint        = {2511.02808v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {We present an application of the Balanced Neural Ratio Estimation (BNRE)
algorithm to improve the statistical validity of parameter estimates used to
characterize the Epoch of Reionization, where the common assumption of a
multivariate Gaussian likelihood leads to overconfident and biased posterior
distributions. Using a two-parameter model of the Ly$\alpha$ forest
autocorrelation function, we show that BNRE yields posterior distributions that
are significantly better calibrated than those obtained under the Gaussian
likelihood assumption, as verified through the Test of Accuracy with Random
Points (TARP) and Simulation-Based Calibration (SBC) diagnostics. These results
demonstrate the potential of Simulation-Based Inference (SBI) methods, and in
particular BNRE, to provide statistically robust parameter constraints within
existing astrophysical modeling frameworks.},
Year          = {2025},
Month         = {Nov},
Url           = {http://arxiv.org/abs/2511.02808v1},
File          = {2511.02808v1.pdf}
}