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arXiv · 2507.06357

Differentiable Stellar Atmospheres with Physics-Informed Neural Networks

Jiadong Li·Mingjie Jian·Yuan-Sen Ting·Gregory M. Green
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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.

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Jiadong Li, Mingjie Jian, Yuan-Sen Ting, Gregory M. Green. 2025-07-08. Differentiable Stellar Atmospheres with Physics-Informed Neural Networks. https://arxiv.org/abs/2507.06357

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