Enterprise AI Analysis
SuryaBench: Benchmark Dataset for Advancing Machine Learning in Heliophysics and Space Weather Prediction
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 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.
Executive Impact & Key Findings
The SuryaBench dataset provides unprecedented detail and scope for advancing AI in heliophysics, offering a robust foundation for critical space weather prediction and research.
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Full Disk Resolution Maintained
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Consistent Temporal Cadence
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Data Spanning a Solar Cycle
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Total Preprocessed Data
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Baseline IoU (AR Segmentation)
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Baseline F1 Score (Flare Prediction)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Active Region Segmentation
This task focuses on identifying active regions (ARs) containing polarity inversion lines (PILs) from full-disk line-of-sight (LoS) magnetograms. The dataset provides 2D binary masks (4096x4096) indicating AR locations, vital for understanding solar activity drivers.
Relevance: ARs are sources of solar flares and CMEs. Accurate segmentation is crucial for monitoring these regions and predicting their eruptive potential, impacting space weather forecasting.
Active Region Emergence Forecasting
Aims to detect ARs before they become visible on the solar surface, providing early warnings for space weather disturbances. The dataset includes time-series data for 56 emerging ARs, with 6 channels (4 acoustic power, magnetic flux, continuum intensity) over 240 timestamps.
Relevance: Early detection of emerging ARs can significantly improve lead times for predicting solar flares, Coronal Mass Ejections (CMEs), and Solar Energetic Particle (SEP) events, safeguarding critical infrastructure.
Coronal Field Extrapolation
Models the 3D structure of the coronal magnetic field using ADAPT-WSA simulations. The dataset includes spherical harmonic coefficients representing the magnetic field, derived from HMI magnetogram data. It covers 5,347 instances over 15 years.
Relevance: Understanding the 3D coronal magnetic field is essential for predicting coronal changes induced by ARs and modeling the propagation of solar phenomena into the heliosphere.
Solar Flare Prediction
A binary classification task to predict M- or X-class solar flares within a 24-hour window. The dataset provides 128,352 labels based on maximum and cumulative flare intensity, derived from GOES X-ray flux measurements.
Relevance: Solar flares can trigger geomagnetic storms, impacting satellites, communications, and power grids. Accurate prediction is critical for operational space weather mitigation efforts.
Solar Wind Speed Estimation
Focuses on predicting solar wind speed at the L1 point, using space-based particle data from SWEPAM and MAG instruments. The dataset comprises 119,225 hourly measurements spanning 15 years.
Relevance: Solar wind modulates Earth's magnetosphere and drives geomagnetic storms. Forecasting its speed helps protect space assets and terrestrial systems from adverse space weather effects.
Solar EUV Spectra Prediction
Predicts solar Extreme Ultraviolet (EUV) irradiance across 1,343 spectral channels using multi-channel SDO/AIA imagery. The dataset includes 189,344 EVE MEGS-A spectra, critical for nowcasting and forecasting.
Relevance: EUV irradiance significantly impacts Earth's ionosphere and thermosphere, affecting satellite drag, communication, and GPS accuracy. Precise prediction supports mission planning and operations.
4096x4096
Native Spatial Resolution Maintained for High-Fidelity Analysis
SuryaBench preserves the full native resolution of SDO observations, enabling high-fidelity analysis for data-driven heliophysics research, unlike previous datasets that reduced resolution.
Data Preparation Workflow for SuryaBench
The SuryaBench dataset undergoes a rigorous preprocessing pipeline to ensure high quality and suitability for machine learning tasks. This workflow harmonizes data from multiple SDO instruments, addressing instrument-specific characteristics and ensuring spatial and temporal consistency.
SDO Data Acquisition (AIA/HMI Level 1)
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AIA Level 1.5 Promotion (Roll, Orbit, Exposure)
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HMI Re-projection & Alignment
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Instrument Degradation Compensation
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Solar Disk Normalization (Fixed Radius)
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Temporal Synchronization (12-min Cadence)
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ML-Ready SuryaBench Dataset
| Property | AIA (Atmospheric Imaging Assembly) | HMI (Helioseismic and Magnetic Imager) |
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| Measurements | Photometric intensity in EUV/UV spectrum across multiple wavebands (e.g., 94Å, 131Å, 171Å, 193Å, 211Å, 304Å, 335Å, 1600Å). | Spectropolarimetric measurements for surface magnetic field (Bx, By, Bz, Blos) and LOS velocity (Vlos). |
| Resolution (Photospheric) | 1.2" (725km) | 1.0" (870km) |
| Native Cadence (Instrumental) | 12s, 24s | 45s, 12m |
| SuryaBench Cadence | 12m | 12m |
| Dynamic Range | 0 to 16,383 DN | ~±4,500 G for B, ~±10^4 m/s for V |
| Key Preprocessing in SuryaBench |
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Calculate Your Potential AI Impact
Estimate the cost savings and efficiency gains your organization could achieve by implementing AI solutions based on heliophysics data analysis, similar to SuryaBench.
Estimated Annual Cost Savings
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Annual Hours Reclaimed
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Your Enterprise AI Roadmap
A strategic overview of how SuryaBench-like data analysis capabilities can be integrated into your enterprise, driving advanced predictive intelligence and operational efficiency.
Phase 1: Data Strategy & Infrastructure Assessment
Evaluate existing data pipelines and infrastructure for compatibility with large-scale, high-resolution scientific datasets. Define data ingestion, storage, and processing requirements. Establish data governance policies for scientific data. Leverage cloud-native solutions for scalability.
Phase 2: ML Model Development & Customization
Develop or adapt machine learning models for specific forecasting and analysis tasks (e.g., space weather impacts, solar energy predictions). Integrate physics-informed AI techniques for enhanced accuracy and interpretability. Benchmark model performance against SuryaBench baselines.
Phase 3: Integration & Operationalization
Integrate validated AI models into existing enterprise systems and workflows. Develop monitoring and alerting mechanisms for real-time insights and predictions. Train operational teams on new AI-driven tools and decision-making processes.
Phase 4: Continuous Improvement & Expansion
Establish a feedback loop for model retraining and performance optimization. Explore new applications and datasets to expand AI capabilities. Monitor the evolving landscape of heliophysics research and ML advancements to maintain a competitive edge.
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