NASA Cosmic Origins · AI/ML Science and Technology Interest Group

Deep Learning for Astrophysics

A hands-on textbook on modern artificial intelligence for astronomy and astrophysics — from autodifferentiation and neural architectures to generative models, simulation-based inference, and autonomous research agents.

23
Chapters
6
Parts
17
Lecturers
Runnable
Code & Outputs
About this book

This Textbook Edition was curated from the lecture series of the NASA Cosmic Origins AI/ML Science and Technology Interest Group (STIG). We homogenized the original lectures, notebooks, and supporting materials into a consistent chapter format while preserving the executable notebooks and their real outputs. Each chapter links to the recording of its original lecture.

The arc runs from computational foundations through the full zoo of deep-learning architectures, into generative modeling and inference, reinforcement learning, and the large-language-model agents now reshaping the research workflow — closing with the broader questions of instrumentation, publishing, and scientific understanding.

Table of Contents

Contributors

Chapter Authors

  • Yuan-Sen Ting3 lectures
  • Phill Cargile2 lectures
  • Carol Cuesta-Lazaro2 lectures
  • Tomasz Rozanski2 lectures
  • Anna Scaife2 lectures
  • John F. Wu2 lectures
  • André Curtis-Trudel1 lecture
  • Gregory Green1 lecture
  • Ryan McClelland1 lecture
  • Daniel Muthukrishna1 lecture
  • Tri Nguyen1 lecture
  • Helen Qu1 lecture
  • Jesse Thaler1 lecture
  • Licia Verde1 lecture
  • Francisco Villaescusa-Navarro1 lecture
  • Duo Xu1 lecture
  • Siyu Yao1 lecture

Leadership Council

  • Yuan-Sen Ting (Co-Chair)The Ohio State University
  • Digvijay Wadekar (Co-Chair)University of Texas at Austin
  • Andrew SaydjariPrinceton University
  • Alex GaglianoMIT
  • Carol Cuesta-LazaroInstitute for Advanced Study at Princeton / Flatiron Institute
  • Georgios ValogiannisUniversity of Chicago
  • Siddharth Mishra-SharmaBoston University