Inversion of CHASE H$α$ Spectral Line during Solar Flares Based on RADYN Dataset via Deep Learning
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arXiv
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| Format: | Preprint |
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2026
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| author | Xu, W. Hao, Q. Zheng, Z. Hong, J. Hu, J. Qiu, Y. Li, C. Ding, M. D. Fang, C. |
| author_facet | Xu, W. Hao, Q. Zheng, Z. Hong, J. Hu, J. Qiu, Y. Li, C. Ding, M. D. Fang, C. |
| contents | Solar flares represent one of the most intense forms of solar activity. Understanding the evolution of physical parameters in the solar atmosphere during flares is key to studying flare mechanisms and improving prediction capabilities. However, directly measuring quantities such as electron number density, temperature, and plasma velocity remains difficult. Here, we introduce a novel fully connected neural network, trained on synthetic data from the Radiative Hydrodynamics Code (RADYN) simulations, to perform rapid inversion of physical parameters from H$α$ spectral profiles. The spectral data were processed to align with the observational resolution of the CHASE satellite, enabling seamless application of the model to real-world observations. Results demonstrate a high degree of consistency with RADYN simulations, achieving low errors under diverse flare conditions. Furthermore, we applied the developed model to analyze CHASE observations of a class X7.1 solar flare on October 1, 2024. The results reveal reasonable spatial and temporal evolution of key parameters throughout different flare phases. This work demonstrates the potential of deep learning techniques for fast and reliable spectral inversion, providing new tools for solar flare diagnostics based on H$α$ data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_26511 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Inversion of CHASE H$α$ Spectral Line during Solar Flares Based on RADYN Dataset via Deep Learning Xu, W. Hao, Q. Zheng, Z. Hong, J. Hu, J. Qiu, Y. Li, C. Ding, M. D. Fang, C. Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics Solar flares represent one of the most intense forms of solar activity. Understanding the evolution of physical parameters in the solar atmosphere during flares is key to studying flare mechanisms and improving prediction capabilities. However, directly measuring quantities such as electron number density, temperature, and plasma velocity remains difficult. Here, we introduce a novel fully connected neural network, trained on synthetic data from the Radiative Hydrodynamics Code (RADYN) simulations, to perform rapid inversion of physical parameters from H$α$ spectral profiles. The spectral data were processed to align with the observational resolution of the CHASE satellite, enabling seamless application of the model to real-world observations. Results demonstrate a high degree of consistency with RADYN simulations, achieving low errors under diverse flare conditions. Furthermore, we applied the developed model to analyze CHASE observations of a class X7.1 solar flare on October 1, 2024. The results reveal reasonable spatial and temporal evolution of key parameters throughout different flare phases. This work demonstrates the potential of deep learning techniques for fast and reliable spectral inversion, providing new tools for solar flare diagnostics based on H$α$ data. |
| title | Inversion of CHASE H$α$ Spectral Line during Solar Flares Based on RADYN Dataset via Deep Learning |
| topic | Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2605.26511 |