Surya: Foundation Model for Heliophysics
Authors:
Sujit Roy,
Johannes Schmude,
Rohit Lal,
Vishal Gaur,
Marcus Freitag,
Julian Kuehnert,
Theodore van Kessel,
Dinesha V. Hegde,
Andrés Muñoz-Jaramillo,
Johannes Jakubik,
Etienne Vos,
Kshitiz Mandal,
Ata Akbari Asanjan,
Joao Lucas de Sousa Almeida,
Amy Lin,
Talwinder Singh,
Kang Yang,
Chetraj Pandey,
Jinsu Hong,
Berkay Aydin,
Thorsten Kurth,
Ryan McGranaghan,
Spiridon Kasapis,
Vishal Upendran,
Shah Bahauddin
, et al. (8 additional authors not shown)
Abstract:
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 desig…
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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.
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Submitted 21 August, 2025; v1 submitted 18 August, 2025;
originally announced August 2025.
SuryaBench: Benchmark Dataset for Advancing Machine Learning in Heliophysics and Space Weather Prediction
Authors:
Sujit Roy,
Dinesha V. Hegde,
Johannes Schmude,
Amy Lin,
Vishal Gaur,
Rohit Lal,
Kshitiz Mandal,
Talwinder Singh,
Andrés Muñoz-Jaramillo,
Kang Yang,
Chetraj Pandey,
Jinsu Hong,
Berkay Aydin,
Ryan McGranaghan,
Spiridon Kasapis,
Vishal Upendran,
Shah Bahauddin,
Daniel da Silva,
Marcus Freitag,
Iksha Gurung,
Nikolai Pogorelov,
Campbell Watson,
Manil Maskey,
Juan Bernabe-Moreno,
Rahul Ramachandran
Abstract:
This paper introduces a high resolution, machine learning-ready heliophysics dataset derived from NASA's Solar Dynamics Observatory (SDO), specifically designed to advance machine learning (ML) applications in solar physics and space weather forecasting. The dataset includes processed imagery from the Atmospheric Imaging Assembly (AIA) and Helioseismic and Magnetic Imager (HMI), spanning a solar c…
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This paper introduces a high resolution, machine learning-ready heliophysics dataset derived from NASA's Solar Dynamics Observatory (SDO), specifically designed to advance machine learning (ML) applications in solar physics and space weather forecasting. The dataset includes processed imagery from the Atmospheric Imaging Assembly (AIA) and Helioseismic and Magnetic Imager (HMI), spanning a solar cycle from May 2010 to July 2024. To ensure suitability for ML tasks, the data has been preprocessed, including correction of spacecraft roll angles, orbital adjustments, exposure normalization, and degradation compensation. We also provide auxiliary application benchmark datasets complementing the core SDO dataset. These provide benchmark applications for central heliophysics and space weather tasks such as active region segmentation, active region emergence forecasting, coronal field extrapolation, solar flare prediction, solar EUV spectra prediction, and solar wind speed estimation. By establishing a unified, standardized data collection, this dataset aims to facilitate benchmarking, enhance reproducibility, and accelerate the development of AI-driven models for critical space weather prediction tasks, bridging gaps between solar physics, machine learning, and operational forecasting.
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Submitted 17 August, 2025;
originally announced August 2025.