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SEPNET (1)

Solar Energetic Particle Forecasting with Multi-Task Deep Learning

Model Description

SEPNET is a multi-task deep learning model that predicts the probability of a solar energetic particle (SEP) event exceeding 10 pfu (≥10 MeV protons) at Earth in the next 24 hours. It uses as input (1) SHARP parameters from SDO/HMI magnetograms (hmi.sharp_720s_nrt) and (2) flare information from the SWPC flare list (LMSAL/HEK). The model uses flare and SHARP features from the preceding 24 hours; it is not triggered by individual CME or flare events. Outputs include a probability value, symmetric uncertainty (from the spread of estimated probabilities over the input window), and an all-clear flag.

Model Figure(s) :

Model Inputs Description

Magnetogram: SDO/HMI SHARP near-real-time product (hmi.sharp_720s_nrt); last data time as available at forecast issue.
Flare catalog: SWPC flare list from LMSAL/HEK (e.g. GOES/XRS); all flares in the 24 hours preceding the issue time are used. Optional NOAA active region numbers when available.

Model Outputs Description

Probability of SEP > 10 pfu (> 10 MeV protons) at Earth in the next 24 hours (mean of model-estimated probability over the preceding 24 h).
Symmetric uncertainty (standard deviation of estimated probability over that window).
All-clear flag (based on probability vs. a configurable threshold).
Prediction window: issue time to issue time + 24 hours.

Model Caveats

Forecasts depend on the availability and timeliness of SHARP and flare catalog data.
Model is trained on historical data; performance may vary during unusual solar activity.
Probability refers to the chance of exceeding 10 pfu; no explicit peak intensity or time-of-peak is given.

Change Log

Initial release. Model outputs probability of SEP >10 pfu in the next 24 hours using SHARP and flare catalog inputs.

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Curator: Chiu Wiegand | NASA Official: Dr. Masha Kuznetsova | Privacy and Security Notices | Accessibility | CCMC Data Collection Consent Agreement