Differentiable Stellar Atmospheres with Physics-Informed Neural Networks

TL;DR

This paper introduces Kurucz-a1, a physics-informed neural network that efficiently models stellar atmospheres under LTE, ensuring physical consistency and enabling advanced data-driven stellar analysis.

Contribution

It develops a differentiable stellar atmosphere model using PINNs with physical constraints, improving accuracy and computational efficiency over traditional methods.

Findings

Kurucz-a1 achieves superior hydrostatic equilibrium.

It produces spectra more consistent with observed solar spectra.

The approach enables optimization of stellar parameters across populations.

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.