Roman Space Telecope WFI Pupil alignment verification
Description
Using Machine Learning (ML) to determine the pupil alignment of the Wide Field Instrument of the Roman Space Telescope during the spacecraft testing in thermal vacuum conditions. The ML algorithm trained on a large data set of possible misalignments to estimate the actual misalignment from the measured data.
Detailed example
This approach outputs a prediction of the RST WFI pupil alignment state.
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
This is a backup technique to verify the RST WFI pupil alignment. Because it is a backup it crates redundancy in the process making it more reliable. A more reliable approach can save the project time and money by completing the verification in a more expedited way. This can ultimately help keep the project on schedule and within cost. This benefits NASA and the public.
Controls / human review
ATO: Not reported; PIA: Not published