NASA · NASA-882 · record R0483

Uniting Physics and Machine Learning for Enhanced Heliophysics Insights

National Aeronautics and Space Administration · GSFC: Goddard Space Flight Center

Overview

Development stage: In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: Not reported
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Science
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

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

Expected benefits

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)

System outputs

3D reconstructed particle tracks and interaction events from SONTRAC detector readouts.

Technology

AI classification: Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
System name(s): Not reported
Custom-developed code: Not reported
Public source code: Not reported

Sourcing

How it was built: Not reported
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: Not reported
Privacy Impact Assessment: Not reported
Authorization to Operate: Not reported
Demographic features: Not reported
Training and evaluation data: Not reported
Federal Data Catalog: Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported