BibTeX

@article{2510.10713v1,
Author        = {Yuan-Sen Ting},
Title         = {Deep Learning in Astrophysics},
Eprint        = {2510.10713v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.IM},
Abstract      = {Deep learning has generated diverse perspectives in astronomy, with ongoing
discussions between proponents and skeptics motivating this review. We examine
how neural networks complement classical statistics, extending our data
analytical toolkit for modern surveys. Astronomy offers unique opportunities
through encoding physical symmetries, conservation laws, and differential
equations directly into architectures, creating models that generalize beyond
training data. Yet challenges persist as unlabeled observations number in
billions while confirmed examples with known properties remain scarce and
expensive. This review demonstrates how deep learning incorporates domain
knowledge through architectural design, with built-in assumptions guiding
models toward physically meaningful solutions. We evaluate where these methods
offer genuine advances versus claims requiring careful scrutiny. - Neural
architectures overcome trade-offs between scalability, expressivity, and data
efficiency by encoding physical symmetries and conservation laws into network
structure, enabling learning from limited labeled data. - Simulation-based
inference and anomaly detection extract information from complex, non-Gaussian
distributions where analytical likelihoods fail, enabling field-level
cosmological analysis and systematic discovery of rare phenomena. - Multi-scale
neural modeling bridges resolution gaps in astronomical simulations, learning
effective subgrid physics from expensive high-fidelity runs to enhance
large-volume calculations where direct computation remains prohibitive. -
Emerging paradigms-reinforcement learning for telescope operations, foundation
models learning from minimal examples, and large language model agents for
research automation-show promise though are still developing in astronomical
applications.},
Year          = {2025},
Month         = {Oct},
Url           = {http://arxiv.org/abs/2510.10713v1},
File          = {2510.10713v1.pdf}
}