Differentiable Stellar Atmospheres with Physics-Informed Neural Networks
Abstract
We present Kurucz-a1, a physics-informed neural network (PINN) that emulates 1D stellar atmosphere models under Local Thermodynamic Equilibrium (LTE), addressing a critical bottleneck in differentiable stellar spectroscopy. By incorporating hydrostatic equilibrium as a physical constraint during training, Kurucz-a1 creates a differentiable atmospheric structure solver that maintains physical consistency while achieving computational efficiency. Kurucz-a1 can achieve superior hydrostatic equilibrium and more consistent with the solar observed spectra compared to ATLAS-12 itself, demonstrating the advantages of modern optimization techniques. Combined with modern differentiable radiative transfer codes, this approach enables data-driven optimization of universal physical parameters across diverse stellar populations-a capability essential for next-generation stellar astrophysics.
Cite
@article{arxiv.2507.06357,
title = {Differentiable Stellar Atmospheres with Physics-Informed Neural Networks},
author = {Jiadong Li and Mingjie Jian and Yuan-Sen Ting and Gregory M. Green},
journal= {arXiv preprint arXiv:2507.06357},
year = {2025}
}
Comments
Accepted at the Workshop on Machine Learning for Astrophysics (ICML 2025)