Differentiable Stellar Atmospheres with Physics-Informed Neural Networks
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911046664454144 |
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| author | Li, Jiadong Jian, Mingjie Ting, Yuan-Sen Green, Gregory M. |
| author_facet | Li, Jiadong Jian, Mingjie Ting, Yuan-Sen Green, Gregory M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06357 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Differentiable Stellar Atmospheres with Physics-Informed Neural Networks Li, Jiadong Jian, Mingjie Ting, Yuan-Sen Green, Gregory M. Solar and Stellar Astrophysics Earth and Planetary Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics 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. |
| title | Differentiable Stellar Atmospheres with Physics-Informed Neural Networks |
| topic | Solar and Stellar Astrophysics Earth and Planetary Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2507.06357 |