Exploring the Early Universe with Deep Learning
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arXiv
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| Format: | Preprint |
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2025
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| author | de Salis, Emmanuel De Santis, Massimo Piras, Davide Giri, Sambit K. Bianco, Michele Cerardi, Nicolas Denzel, Philipp Selcuk-Simsek, Merve Hess, Kelley M. Toribio, M. Carmen Kirsten, Franz Ghorbel, Hatem |
| author_facet | de Salis, Emmanuel De Santis, Massimo Piras, Davide Giri, Sambit K. Bianco, Michele Cerardi, Nicolas Denzel, Philipp Selcuk-Simsek, Merve Hess, Kelley M. Toribio, M. Carmen Kirsten, Franz Ghorbel, Hatem |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22018 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring the Early Universe with Deep Learning de Salis, Emmanuel De Santis, Massimo Piras, Davide Giri, Sambit K. Bianco, Michele Cerardi, Nicolas Denzel, Philipp Selcuk-Simsek, Merve Hess, Kelley M. Toribio, M. Carmen Kirsten, Franz Ghorbel, Hatem Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics Machine Learning 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. |
| title | Exploring the Early Universe with Deep Learning |
| topic | Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2509.22018 |