More data than you want, fewer data than you need: machine learning approaches to starlight subtraction with MagAO-X

Joseph D. Long; Jared R. Males; Laird M. Close; Olivier Guyon; Sebastiaan Y. Haffert; Alycia J. Weinberger; Jay Kueny; Kyle Van Gorkom; Eden McEwen; Logan Pearce; Maggie Kautz; Jialin Li; Jennifer Lumbres; Alexander Hedglen; Lauren Schatz; Avalon McLeod; Isabella Doty; Warren B. Foster; Roswell Roberts; Katie Twitchell

doi:10.1117/12.3018424

27 August 2024 More data than you want, fewer data than you need: machine learning approaches to starlight subtraction with MagAO-X

Joseph D. Long, Jared R. Males, Laird M. Close, Olivier Guyon, Sebastiaan Y. Haffert, Alycia J. Weinberger, Jay Kueny, Kyle Van Gorkom, Eden McEwen, Logan Pearce, Maggie Kautz, Jialin Li, Jennifer Lumbres, Alexander Hedglen, Lauren Schatz, Avalon McLeod, Isabella Doty, Warren B. Foster, Roswell Roberts, Katie Twitchell

Author Affiliations +

Proceedings Volume 13097, Adaptive Optics Systems IX; 130972X (2024) https://doi.org/10.1117/12.3018424
Event: SPIE Astronomical Telescopes + Instrumentation, 2024, Yokohama, Japan

Abstract

High-contrast imaging data analysis depends on removing residual starlight from the host star to reveal planets and disks. Most observers do this with principal components analysis (i.e. KLIP) using modes computed from the science images themselves. These modes may not be orthogonal to planet and disk signals, leading to over-subtraction. The wavefront sensor data recorded during the observation provide an independent signal with which to predict the instrument point-spread function (PSF). MagAO-X is an extreme adaptive optics (ExAO) system for the 6.5-meter Magellan Clay telescope and a technology pathfinder for ExAO with GMagAO-X on the upcoming Giant Magellan Telescope. MagAO-X is designed to save all sensor information, including kHz-speed wavefront measurements. Our software and compressed data formats were designed to record the millions of training samples required for machine learning with high throughput. The large volume of image and sensor data lets us learn a PSF model incorporating all the information available. This allows us to probe smaller star-planet separations at greater sensitivities, which will be needed for rocky planet imaging.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Joseph D. Long, Jared R. Males, Laird M. Close, Olivier Guyon, Sebastiaan Y. Haffert, Alycia J. Weinberger, Jay Kueny, Kyle Van Gorkom, Eden McEwen, Logan Pearce, Maggie Kautz, Jialin Li, Jennifer Lumbres, Alexander Hedglen, Lauren Schatz, Avalon McLeod, Isabella Doty, Warren B. Foster, Roswell Roberts, and Katie Twitchell "More data than you want, fewer data than you need: machine learning approaches to starlight subtraction with MagAO-X", Proc. SPIE 13097, Adaptive Optics Systems IX, 130972X (27 August 2024); https://doi.org/10.1117/12.3018424

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