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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.14112 |
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| _version_ | 1866908497277353984 |
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| author | Roy, Sujit Schmude, Johannes Lal, Rohit Gaur, Vishal Freitag, Marcus Kuehnert, Julian van Kessel, Theodore Hegde, Dinesha V. Muñoz-Jaramillo, Andrés Jakubik, Johannes Vos, Etienne Mandal, Kshitiz Asanjan, Ata Akbari Almeida, Joao Lucas de Sousa Lin, Amy Singh, Talwinder Yang, Kang Pandey, Chetraj Hong, Jinsu Aydin, Berkay Kurth, Thorsten McGranaghan, Ryan Kasapis, Spiridon Upendran, Vishal Bahauddin, Shah da Silva, Daniel Pogorelov, Nikolai V. Spalding, Anne Watson, Campbell Maskey, Manil Guhathakurta, Madhulika Bernabe-Moreno, Juan Ramachandran, Rahul |
| author_facet | Roy, Sujit Schmude, Johannes Lal, Rohit Gaur, Vishal Freitag, Marcus Kuehnert, Julian van Kessel, Theodore Hegde, Dinesha V. Muñoz-Jaramillo, Andrés Jakubik, Johannes Vos, Etienne Mandal, Kshitiz Asanjan, Ata Akbari Almeida, Joao Lucas de Sousa Lin, Amy Singh, Talwinder Yang, Kang Pandey, Chetraj Hong, Jinsu Aydin, Berkay Kurth, Thorsten McGranaghan, Ryan Kasapis, Spiridon Upendran, Vishal Bahauddin, Shah da Silva, Daniel Pogorelov, Nikolai V. Spalding, Anne Watson, Campbell Maskey, Manil Guhathakurta, Madhulika Bernabe-Moreno, Juan Ramachandran, Rahul |
| contents | Heliophysics is central to understanding and forecasting space weather events and solar activity. Despite decades of high-resolution observations from the Solar Dynamics Observatory (SDO), most models remain task-specific and constrained by scarce labeled data, limiting their capacity to generalize across solar phenomena. We introduce Surya, a 366M parameter foundation model for heliophysics designed to learn general-purpose solar representations from multi-instrument SDO observations, including eight Atmospheric Imaging Assembly (AIA) channels and five Helioseismic and Magnetic Imager (HMI) products. Surya employs a spatiotemporal transformer architecture with spectral gating and long--short range attention, pretrained on high-resolution solar image forecasting tasks and further optimized through autoregressive rollout tuning. Zero-shot evaluations demonstrate its ability to forecast solar dynamics and flare events, while downstream fine-tuning with parameter-efficient Low-Rank Adaptation (LoRA) shows strong performance on solar wind forecasting, active region segmentation, solar flare forecasting, and EUV spectra. Surya is the first foundation model in heliophysics that uses time advancement as a pretext task on full-resolution SDO data. Its novel architecture and performance suggest that the model is able to learn the underlying physics behind solar evolution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_14112 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Surya: Foundation Model for Heliophysics Roy, Sujit Schmude, Johannes Lal, Rohit Gaur, Vishal Freitag, Marcus Kuehnert, Julian van Kessel, Theodore Hegde, Dinesha V. Muñoz-Jaramillo, Andrés Jakubik, Johannes Vos, Etienne Mandal, Kshitiz Asanjan, Ata Akbari Almeida, Joao Lucas de Sousa Lin, Amy Singh, Talwinder Yang, Kang Pandey, Chetraj Hong, Jinsu Aydin, Berkay Kurth, Thorsten McGranaghan, Ryan Kasapis, Spiridon Upendran, Vishal Bahauddin, Shah da Silva, Daniel Pogorelov, Nikolai V. Spalding, Anne Watson, Campbell Maskey, Manil Guhathakurta, Madhulika Bernabe-Moreno, Juan Ramachandran, Rahul Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics Artificial Intelligence Heliophysics is central to understanding and forecasting space weather events and solar activity. Despite decades of high-resolution observations from the Solar Dynamics Observatory (SDO), most models remain task-specific and constrained by scarce labeled data, limiting their capacity to generalize across solar phenomena. We introduce Surya, a 366M parameter foundation model for heliophysics designed to learn general-purpose solar representations from multi-instrument SDO observations, including eight Atmospheric Imaging Assembly (AIA) channels and five Helioseismic and Magnetic Imager (HMI) products. Surya employs a spatiotemporal transformer architecture with spectral gating and long--short range attention, pretrained on high-resolution solar image forecasting tasks and further optimized through autoregressive rollout tuning. Zero-shot evaluations demonstrate its ability to forecast solar dynamics and flare events, while downstream fine-tuning with parameter-efficient Low-Rank Adaptation (LoRA) shows strong performance on solar wind forecasting, active region segmentation, solar flare forecasting, and EUV spectra. Surya is the first foundation model in heliophysics that uses time advancement as a pretext task on full-resolution SDO data. Its novel architecture and performance suggest that the model is able to learn the underlying physics behind solar evolution. |
| title | Surya: Foundation Model for Heliophysics |
| topic | Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics Artificial Intelligence |
| url | https://arxiv.org/abs/2508.14112 |