NASA · NASA-862 · record R0465

Machine Learning effort to calculate Parker Solar Probe magnetometer offsets

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

Testing various ML algorithms to model the magnetometer offset values at points in the orbit where the traditional methods are not available.

Expected benefits

The previous methods of offset calculations for the magnetometer are not applicable as the probe passes through its closest approach to the sun each orbit. This is also the primary science period of the mission. We are trying to find an alternative method based on housekeeping information from the spacecraft itself since that is the source of the offsets affecting the magnetometer: the spacecraft contribution to the magnetic field readings.

System outputs

Our ML model tries to relate these offset values (calculated using factors outside the spacecraft) to the status of the spacecraft systems and narrow in on which systems are contributing to the offset values. We were able to create a model that averaged within 4 nT of the traditional method values. Unfortunately, the sparseness of the Alfvenic wave detections limited the accuracy of the model as they are only detected at most once per day. Given this limitation, we are please we were able to get within 4 nT of the traditional method.

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