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

arXiv: https://arxiv.org/abs/2508.14107 · HTML: https://mlsanity.com/paper/2508.14107/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 66 references that are in the map.
- First posted on arXiv 18 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [A Machine Learning Dataset Prepared From the NASA Solar Dynamics Observatory Mission](https://mlsanity.com/paper/1903.04538/) (2019)
- [Machine learning in solar physics](https://mlsanity.com/paper/2306.15308/) (2023)
- [Advancing Solar Flare Prediction using Deep Learning with Active Region Patches](https://mlsanity.com/paper/2406.11054/) (2024)
- [AI Foundation Model for Heliophysics: Applications, Design, and Implementation](https://mlsanity.com/paper/2410.10841/) (2024)
- [Predicting the Emergence of Solar Active Regions Using Machine Learning](https://mlsanity.com/paper/2402.08890/) (2024)

## Built on by (most cited first, 4 of 4)

- [SolARED: Solar Active Region Emergence Dataset for Machine Learning Aided Predictions](https://mlsanity.com/paper/2601.13145/) (2026)
- [Review of Machine Learning Models for Solar Energetic Particle Prediction](https://mlsanity.com/paper/2606.19539/) (2026)
- [SomBench: Benchmark Dataset for Advancing Machine Learning in Lunar Science](https://mlsanity.com/paper/2609.13277/) (2026)
- [Cross-Modal Solar Image Synthesis: Adapting the Surya Foundation Model from He I 10830 AA to EUV Translation and Coronal Hole Segmentation](https://mlsanity.com/paper/2610.04553/) (2026)

## Abstract (arXiv)

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.