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Deep Learning for Astrophysics An Open Textbook from the NASA Cosmic Origins AI/ML Science and Technology Interest Group

YS Ting, D Wadekar, P Cargile… - arXiv preprint arXiv …, 2026 - arxiv.org
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… across six parts from 17 lecturers, running from computational foundations and deep-learning architectures through generative modeling, simulation-based inference, and …

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@article{2606.30855v1,
Author        = {Yuan-Sen Ting and Digvijay Wadekar and Phill Cargile and Carol Cuesta-Lazaro and André Curtis-Trudel and Gregory Green and Ryan McClelland and Daniel Muthukrishna and Tri Nguyen and Helen Qu and Tomasz Rozanski and Anna Scaife and Jesse Thaler and Licia Verde and Francisco Villaescusa-Navarro and John F. Wu and Duo Xu and Siyu Yao and Alex Gagliano and Siddharth Mishra-Sharma and Andrew K. Saydjari and Georgios Valogiannis and Peter Kurczynski and Swara Ravindranath},
Title         = {Deep Learning for Astrophysics: An Open Textbook from the NASA Cosmic Origins AI/ML Science and Technology Interest Group},
Eprint        = {2606.30855v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.IM},
Abstract      = {The astronomical community's ability to use modern machine learning shapes the science return of upcoming facilities. Recent community assessments single out education as the principal barrier to adoption, because what limits uptake is the uneven understanding of these methods rather than their availability. The NASA Cosmic Origins Artificial Intelligence and Machine Learning Science and Technology Interest Group (AI/ML STIG) was formed to address this gap through short, domain-specific tutorials and community discussion. We present Deep Learning for Astrophysics, a freely available textbook at https://deeplearning4astro.com, curated from the group's lecture series. It collects 23 chapters across six parts from 17 lecturers, running from computational foundations and deep-learning architectures through generative modeling, simulation-based inference, and reinforcement learning to large-language-model agents, and closing with the practice of AI-laden science. Many chapters include executable notebooks. We also outline the group's plan for the coming year, centered on agentic research and on NASA's ASTRA mission-concept initiative.},
Year          = {2026},
Month         = {Jun},
Url           = {http://arxiv.org/abs/2606.30855v1},
File          = {2606.30855v1.pdf}
}

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