Physics informed ai detects solar wind space weather events early
PISCES: Physics-Informed Solar-wind Convolutional autoEncoder for Space-weather Anomaly Detection and Early Warning
Machine LearningArtificial Intelligence
Summary
Space weather events caused by bursts of solar wind can affect Earth, so detecting these early is important. The authors developed an AI model called PISCES that learns from measurements of the solar wind while respecting known physics relationships. Instead of one generic alert, it reports detailed scores showing what kind of changes occurred in magnetic fields and plasma. This detailed early warning can help identify sudden events that might impact Earth before they arrive.
What this means in practice
- •For space weather forecasters: Generate early, physics-based alerts for solar wind disturbances before they reach Earth, improving advance warning of geomagnetic storms.
- •For satellite operators: Use PISCES alerts to better prepare satellite systems for upcoming solar wind anomalies that may disrupt operations or damage hardware.
Authors
Kevin Lee, Alison J. March
Abstract
Space weather early warning depends on detecting solar wind transients in in-situ measurements at the first Sun-Earth Lagrange point (L1), before they reach Earth. Fixed thresholds can miss combined magnetic and plasma structure, and many learning methods provide a single anomaly score. We present the Physics-Informed Solar-wind Convolutional autoEncoder for Space-weather (PISCES), a convolutional autoencoder trained without catalog labels on OMNI solar wind measurements under physics constraints. Its loss includes magnetic field consistency, an empirical relation between temperature and velocity, the Parker spiral angle, and penalties on changes between consecutive one-minute samples in derived quantities calculated from the reconstruction. At inference, PISCES separates the anomaly score into magnetic and plasma reconstruction errors, physics relations, and residual corrections, and reports the magnitude of each contribution. Attenuation of the skip connections, selected on validation data, improves average precision for the trained models, while the untrained scores remain nearly the same. The trained models also give a more consistent ordering of these physical contributions. After smoothing with a trailing median, the alarms can precede independently observed sudden commencements, including positive sudden impulses.