Metraipolis › archive › dc_ff7653afabec

Physics-Informed Neural Networks for Fast Multilayer Spectral Inversion of Hα 6562.8 A and Ca II 8542.1 A Spectra — arXiv:2609.18025

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.18025
License (as archived): CC0-1.0
Archived: 2026-10-15T15:07:53.078Z
SHA-256 of the archived text: 963224d3a2c2fda1a3dc4b571676d7f600cff9b1686bf7268ae09f9a6333b0b6
Stable URL of this page: https://metraipolis.com/documento/dc_ff7653afabec

How to cite

Physics-Informed Neural Networks for Fast Multilayer Spectral Inversion of Hα 6562.8 A and Ca II 8542.1 A Spectra — arXiv:2609.18025 — arXiv (en), https://arxiv.org/abs/2609.18025 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ff7653afabec (Metraipolis archive dc_ff7653afabec, sha256 963224d3a2c2fda1a3dc4b571676d7f600cff9b1686bf7268ae09f9a6333b0b6)

BibTeX

@misc{dcff7653afabec,
  title = {Physics-Informed Neural Networks for Fast Multilayer Spectral Inversion of Hα 6562.8 A and Ca II 8542.1 A Spectra — arXiv:2609.18025},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.18025},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ff7653afabec; sha256 963224d3a2c2fda1a3dc4b571676d7f600cff9b1686bf7268ae09f9a6333b0b6},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.18025. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Strong chromospheric absorption lines such as H$\alpha$ 6562.8 A and Ca II 8542.1 A provide vital diagnostics of plasma dynamics and thermal structure in the solar chromosphere. Multilayer spectral inversion (MLSI) offers a physically interpretable framework for modeling these lines using a finite number of radiative-transfer layers, but conventional MLSI relies on pixel-by-pixel nonlinear least-squares fitting, making it computationally expensive for large imaging spectroscopic data sets. Here, we introduce a physics-informed neural-network (PINN) framework to accelerate MLSI while preserving its analytic radiative-transfer formulation. The network predicts MLSI parameters directly from observed line profiles and passes them through a differentiable MLSI forward model to synthesize spectra. Training follows a two-stage approach: an initial stage optimized solely via spectral reconstruction loss, followed by fine-tuning that combines spectral consistency with parameter-space supervision from conventional MLSI results on a single reference image. This strategy eliminates the need for large precomputed training sets while maintaining physical interpretability. Applied to Fast Imaging Solar Spectrograph (FISS) observations from the Goode Solar Telescope (GST) targeting both quiet-Sun and active-region regions, MLSI-PINN parameter maps reproduce the primary spatial structures of direct inversions, achieving an arithmetic mean pixel-wise Pearson correlation coefficient of 0.933 across all evaluated parameters. The reconstructed spectra closely match both observed profiles and conventional MLSI fits. Post-training, MLSI-PINN processes a raster in approximately 5-15 seconds compared to 3-5 minutes for conventional MLSI, delivering an inference speedup of about 12-60 times without substantial loss in reconstruction quality, enabling efficient MLSI analysis on large chromospheric data sets.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-17 in cs.LG).
Categorie arXiv: Solar and Stellar Astrophysics (astro-ph.SR) ; Instrumentation and Methods for Astrophysics (astro-ph.IM); Artificial Intelligence (cs.AI); Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.18025 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T14:08:19.228Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-17?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).