Deep learning-driven likelihood-free parameter inference for 21-cm forest observations

TL;DR

This paper presents a deep learning-based likelihood-free inference method using normalizing flows to efficiently analyze 21-cm forest data, enabling constraints on dark matter and early universe properties with minimal simulations.

Contribution

It introduces a novel deep learning framework employing normalizing flows for parameter inference from non-Gaussian 21-cm forest signals, reducing computational costs and improving accuracy.

Findings

Accurately recovers posterior distributions from simulated SKA data.

Demonstrates efficiency with minimal simulations for complex non-Gaussian signals.

Provides a robust tool for probing dark matter and cosmic heating history.

Abstract

The hyperfine structure absorption lines of neutral hydrogen in spectra of high-redshift radio sources, known collectively as the 21-cm forest, have been demonstrated as a sensitive probe to the small-scale structures governed by the dark matter (DM) properties, as well as the thermal history of the intergalactic medium regulated by the first galaxies during the epoch of reionization. By statistically analyzing these spectral features, the one-dimensional (1D) power spectrum of the 21-cm forest can effectively break the parameter degeneracies and constrain the properties of both DM and the first galaxies. However, conventional parameter inference methods face challenges due to computationally expensive simulations for 21-cm forest and the non-Gaussian signal characteristics. To address these issues, we introduce generative normalizing flows for data augmentation and inference normalizing flows for parameters estimation. This approach efficiently estimates parameters from minimally simulated datasets with non-Gaussian signals. Using simulated data from the upcoming Square Kilometre Array (SKA), we demonstrate the ability of the deep learning-driven likelihood-free approach to generate accurate posterior distributions, providing a robust and efficient tool for probing DM and the cosmic heating history using the 1D power spectrum of 21-cm forest in the era of SKA. This methodology is adaptable for scientific analyses with other unevenly distributed data.