Machine Learning Algorithms for Alignment Verification of the Roman Space Telescope

Joseph M. Howard; Robert Campion; Samuel Nissim; Tuong Phong; Ariba Khan; Brian Daley; Sam Becker; Scott Rohrbach; Margaret Dominguez

NASA NTRS · 20240005625

Machine Learning Algorithms for Alignment Verification of the Roman Space Telescope

Joseph M. Howard·Robert Campion·Samuel Nissim·Tuong Phong·Ariba Khan·Brian Daley·Sam Becker·Scott Rohrbach·Margaret Dominguez

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Abstract

The Nancy Grace Roman Telescope is a NASA observatory designed to unravel the secrets of dark energy and dark matter, search for and image exoplanets, and explore many topics in infrared optics. Scheduled to launch no earlier than October 2026, this 2.4 meter aperture telescope has a field of view 100 times greater than the Hubble Space Telescope. The mission is currently in its construction phase, where the telescope and its two instruments will soon be aligned together to ensure proper pupil matching. To help verify this alignment, multiple point sources above the entrance pupil of the telescope will illuminate the optical path through the telescope-instrument system, and shadows of various obstructions in the system will be analyzed using machine learning algorithms to determine the pupil matching error. This presentation discusses the test approach and the machine learning algorithms employed, as well as our uncertainty predictions based on a modeled Monte-Carlo analysis of the test.

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Joseph M. Howard, Robert Campion, Samuel Nissim, Tuong Phong, Ariba Khan, Brian Daley, Sam Becker, Scott Rohrbach, Margaret Dominguez. Machine Learning Algorithms for Alignment Verification of the Roman Space Telescope. https://ntrs.nasa.gov/citations/20240005625

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