Point spread function (PSF) calibration using machine learning (ML) techniques.
Description
The Habitable Worlds Observatory (HWO) aims to image and thoroughly characterize exoEarths and is the highest priority of NASA as recommended by the Astro2020 Decadal Survey. With this goal in mind, NASA HQ has directed NASA GSFC to open a program office to facilitate advancing technologies related to HWO. To achieve the target of imaging dim exoplanets (to the order of 10^10 as compared to host star) it is necessary to create and maintain high-contrast zones in the science camera. This imposes stringent constraints on the telescope requirements. At GSFC, we are working on point spread function (PSF) calibration using machine learning (ML) techniques. oUse wavefront sensor camera images as input and science image to train various ML methods.
Detailed example
Mapping between wavefront senor images to science images
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
Cost and time saving.
Controls / human review
ATO: Not reported; PIA: Not published