Uniting Physics and Machine Learning for Enhanced Heliophysics Insights
Description
The Solar Neutron TRACking (SONTRAC) instrument is designed to detect incident solar neutrons in an energy range that fills a key gap in understanding flare ion acceleration. SONTRAC tracks recoil protons generated by neutron interactions as they traverse a fiber bundle volume, depositing ionization energy along their paths. Current reconstruction of proton tracks—determining energy deposition and momentum vectors—is labor-intensive and ambiguous
Detailed example
3D reconstructed particle tracks and interaction events from SONTRAC detector readouts.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Autonomous neutron event reconstruction in SONTRAC will significantly reduce manual analysis time, increase the fraction of usable neutron interaction events, and improve instrument efficiency. For NASA, this translates to higher-fidelity science return from the same mission resources, enabling better understanding of solar flare particle acceleration. For the broader public, improved solar particle monitoring enhances space weather forecasting, which protects satellite operations, communications, and astronaut safety. Key performance indicators (KPIs): •Increased percentage of reconstructed neutron events (higher detection efficiency) •Reduction in manual reconstruction effort (time savings for analysts) •Improved accuracy and reliability of neutron track reconstruction (science quality ROI)
Controls / human review
ATO: Not reported; PIA: Not published