Machine Learning and the SKA for Cosmic Dawn and the Epoch of Reionization
Anshuman Acharya, Michele Bianco, Daniela Breitman, Huaxi Chen, Abhirup Datta, Kangning Diao, Sambit K. Giri, Caroline S. Heneka, Nicholas Kern, Adrian Liu, Yashrajsinh Mahida, Suman Majumdar, Samit Kumar Pal, Shulei Ni, Yannic Pietschke, Davide Piras, Abinash Kumar Shaw, Hayato Shimabukuro, Ce Sui, Anshuman Tripathi, Xiaosheng Zhao
astro-ph.IM, astro-ph.CO
Submitted: 2026-07-03
Comments: Published in Advancing Astrophysics with the SKA II (AASKAII), 2026 (arXiv:2606.20366). Report-no:AASKAII/Acharya02. Advancing Astrophysics with the SKA II (AASKAII) outlines the transformative scientific advances that will be enabled by the SKA telescopes
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
The gist: When operational, the SKA will generate unprecedented amounts of data and provide exquisite sensitivity for 21 cm tomography of Cosmic Dawn (CD) and the Epoch of Reionization (EoR).
Terminology
Abstract
When operational, the SKA will generate unprecedented amounts of data and provide exquisite sensitivity for 21 cm tomography of Cosmic Dawn (CD) and the Epoch of Reionization (EoR). With this comes opportunities for new data-driven algorithms that unlock new methods for instrument modelling, data analysis, theoretical simulation, and inference for understanding the high-redshift universe. In this chapter, we provide an overview of some machine learning algorithms that have been proposed for CD and EoR science with the SKA
Sources
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