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<p>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 December 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 Extreme Ultraviolet (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.</p>

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SuryaBench: Benchmark Dataset for Advancing Machine Learning in Heliophysics and Space Weather Prediction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月2日

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  • Sujit Roy,
  • Dinesha V. Hegde,
  • Johannes Schmude,
  • Rohit Lal,
  • Vishal Gaur,
  • Amy Lin,
  • 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

摘要

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 December 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 Extreme Ultraviolet (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.