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Deep learning-driven likelihood-free parameter inference for 21-cm forest observations
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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.
Forward citations
Cited by 5 Pith papers
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Using an analytic halo model, the authors forecast that 21-cm forest observations with SKA-LOW could constrain the total neutrino mass to about 0.1 eV, potentially distinguishing neutrino mass hierarchies.
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A Fisher forecast from 21cmFAST simulations shows 21cm variance can constrain the CDM isocurvature fraction to about 3e-4 with SKA, while skewness is an order of magnitude weaker.
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Prospects of a statistical detection of the 21-cm forest and its potential to constrain the thermal state of the neutral IGM during reionization
Forward-modeled 21-cm forest spectra show that a 1D power spectrum detection is feasible with 500 hr of uGMRT or 50 hr of SKA1-low time for a 25% neutral, cold IGM at z=6, and that null detections can constrain IGM he...
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Analytical modeling of the one-dimensional power spectrum of 21-cm forest based on a halo model method
A halo-model formula for the 1D power spectrum of the 21-cm forest is presented and shown to match small-scale simulations built from the same gas and temperature assumptions.
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