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The redshifted 21 cm signal from the Epoch of Reionization (EoR) directly probes the ionization and thermal states of the intergalactic medium during that period. In particular, the distribution of the ionized regions around the radiating sources during EoR introduces scale-dependent features in the spherically averaged EoR 21 cm signal power spectrum. Aims. The goal is to study these scale-dependent features at different stages of reionization using numerical simulations and to build a source model-independent framework to probe the properties of the intergalactic medium using EoR 21 cm signal power spectrum measurements. Methods. Under the assumption of high spin temperature, we modeled the redshift evolution of the ratio of the EoR 21 cm brightness temperature power spectrum to the corresponding density power spectrum using an ansatz consisting of a set of redshift and scale-independent parameters. This set of eight parameters probes the redshift evolution of the average ionization fraction and the quantities related to the morphology of the ionized regions. Results. We tested this ansatz on different reionization scenarios generated using different simulation algorithms and found that it is able to recover the redshift evolution of the average neutral fraction within an absolute deviation ≲0.1. Conclusions. Our framework allows us to interpret 21 cm signal power spectra in terms of parameters related to the state of the IGM. This source model-independent framework is able to efficiently constrain reionization scenarios using multi-redshift power spectrum measurements with ongoing and future radio telescopes such as LOFAR, MWA, HERA, and SKA. This will add independent information regarding the EoR IGM properties.
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This content will become publicly available on June 1, 2026
Inferring IGM parameters from the redshifted 21-cm power spectrum using Artificial Neural Networks
The high redshift 21-cm signal promises to be a crucial probe of the state of the intergalactic medium (IGM). Understanding the connection between the observed 21-cm power spectrum and the physical quantities intricately associated with the IGM is crucial to fully understand the evolution of our Universe. In this study, we develop an emulator using artificial neural network (ANN) to predict the 21-cm power spectrum from a given set of IGM properties, namely, the bubble size distribution and the volume averaged ionization fraction. This emulator is implemented within a standard Bayesian framework to constrain the IGM parameters from a given 21-cm power spectrum.We compare the performance of the Bayesian method to an alternate method using ANN to predict the IGM parameters from a given input power spectrum, and find that both methods yield similar levels of accuracy, while the ANN is significantly faster. We also use this ANN method of parameter estimation to predict the IGM parameters from a test set contaminated with noise levels expected from the SKA-LOW instrument after 1000 hours of observation. Finally, we train a separate ANN to predict the source parameters from the IGM parameters directly, at a redshift ofz= 9.1, demonstrating the possibility of a non-analytic inference of the source parameters from the IGM parameters for the first time. We achieve high accuracies, with R2-scores ranging between 0.898–0.978 for the ANN emulator and between 0.966–0.986 and 0.817–0.981 for the predictions of IGM parameters from 21-cm power spectrum and source parameters from IGM parameters, respectively. The predictions of the IGM parameters from the Bayesian method incorporating the ANN emulator leads to tight constraints on the IGM parameters.
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- Award ID(s):
- 2206602
- PAR ID:
- 10632330
- Publisher / Repository:
- IOP Publishing Ltd
- Date Published:
- Journal Name:
- Journal of Cosmology and Astroparticle Physics
- Volume:
- 2025
- Issue:
- 06
- ISSN:
- 1475-7516
- Page Range / eLocation ID:
- 003
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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