Machine Learning effort to calculate Parker Solar Probe magnetometer offsets
Description
Testing various ML algorithms to model the magnetometer offset values at points in the orbit where the traditional methods are not available.
Detailed example
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.
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
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.
Controls / human review
ATO: Not reported; PIA: Not published