Inferring IGM parameters from the redshifted 21-cm Power Spectrum using Artificial Neural Networks

TL;DR

This paper develops an artificial neural network emulator within a Bayesian framework to efficiently infer IGM parameters from the 21-cm power spectrum, demonstrating high accuracy and speed, even with realistic noise levels.

Contribution

It introduces a novel ANN-based emulator and inference method for IGM parameters from 21-cm data, enabling fast and accurate analysis including source parameter prediction.

Findings

ANN emulator achieves high R2-scores (0.898-0.978).

Methods yield similar accuracy but ANN is faster.

Accurate IGM parameter constraints with ~±0.14 error bars.

Abstract

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 of $z=9.1$, 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 with error bars around $\pm{0.14}$ on the IGM parameters.