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

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.

70 pages, 18 figures; accepted for publication in Communications Physics

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Cited by in corpus (5)

• Prospects of a statistical detection of the 21-cm forest and its potential to constrain the thermal state of the neutral IGM during reionization
• Impact of 21-cm foreground mitigation strategies on reionization power spectrum constraints
• Conditional variational autoencoders for cosmological model discrimination and anomaly detection in cosmic microwave background power spectra
• Efficient neutral-IGM inference from noisy 21-cm forest spectra with latent-space U-Net encoding and XGBoost
• Parameter inference of millilensed gravitational waves using neural spline flows