BibTeX

@article{2606.30855v2,
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        = {2606.30855v2},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.IM},
Abstract      = {Recent community assessments identify education as a principal barrier to adopting modern machine learning in astronomy. We present Deep Learning for Astrophysics, a freely available textbook at https://deeplearning4astro.com, curated from the NASA Cosmic Origins Artificial Intelligence and Machine Learning Science and Technology Interest Group (AI/ML STIG) lecture series. The book collects 23 chapters by 17 lecturers across six parts, moving from computational foundations and deep-learning architectures through generative modeling, simulation-based inference, reinforcement learning, and large-language-model agents to the practice of AI-laden science. Many include executable notebooks using astronomical data.},
Year          = {2026},
Month         = {Jun},
Url           = {http://arxiv.org/abs/2606.30855v2},
File          = {2606.30855v2.pdf}
}