Exploring the Early Universe with Deep Learning

Abstract

Hydrogen is the most abundant element in our Universe. The first generation of stars and galaxies produced photons that ionized hydrogen gas, driving a cosmological event known as the Epoch of Reionization (EoR). The upcoming Square Kilometre Array Observatory (SKAO) will map the distribution of neutral hydrogen during this era, aiding in the study of the properties of these first-generation objects. Extracting astrophysical information will be challenging, as SKAO will produce a tremendous amount of data where the hydrogen signal will be contaminated with undesired foreground contamination and instrumental systematics. To address this, we develop the latest deep learning techniques to extract information from the 2D power spectra of the hydrogen signal expected from SKAO. We apply a series of neural network models to these measurements and quantify their ability to predict the history of cosmic hydrogen reionization, which is connected to the increasing number and efficiency of early photon sources. We show that the study of the early Universe benefits from modern deep learning technology. In particular, we demonstrate that dedicated machine learning algorithms can achieve more than a $0.95$ $R^2$ score on average in recovering the reionization history. This enables accurate and precise cosmological and astrophysical inference of structure formation in the early Universe.

EPIA 2025 preprint version, 12 pages, 3 figures

References in corpus (17)

• Gaussian Error Linear Units (GELUs)
• Cosmology at Low Frequencies: The 21 cm Transition and the High-Redshift Universe
• Normalizing Flows: An Introduction and Review of Current Methods
• 21cmFAST: A Fast, Semi-Numerical Simulation of the High-Redshift 21-cm Signal
• Upper limits on the 21-cm Epoch of Reionization power spectrum from one night with LOFAR
• Inferring the astrophysics of reionization and cosmic dawn from galaxy luminosity functions and the 21-cm signal
• The Cosmic Dawn and Epoch of Reionization with the Square Kilometre Array
• Optimal identification of HII regions during reionization in 21-cm observations
• Deep learning approach for identification of HII regions during reionization in 21-cm observations
• Exploring the likelihood of the 21-cm power spectrum with simulation-based inference
• KiDS-SBI: Simulation-based inference analysis of KiDS-1000 cosmic shear
• Deep learning approach for identification of HII regions during reionization in 21-cm observations -- II. foreground contamination
• Extracting the Global 21-cm signal from Cosmic Dawn and Epoch of Reionization in the presence of Foreground and Ionosphere
• Simulation-based inference of single-molecule experiments
• Analysing the Epoch of Reionization with three-point correlation functions and machine learning techniques
• Full-waveform earthquake source inversion using simulation-based inference
• Differentiating Warm Dark Matter Models through 21cm Line Intensity Mapping: A Convolutional Neural Network Approach