More data than you want, less data than you need: machine learning approaches to starlight subtraction with MagAO-X
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| Format: | Preprint |
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2024
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| _version_ | 1866910532295983104 |
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| author | Long, Joseph D. Males, Jared R. Close, Laird M. Guyon, Olivier Haffert, Sebastiaan Y. Weinberger, Alycia J. Kueny, Jay Van Gorkom, Kyle McEwen, Eden Pearce, Logan Kautz, Maggie Li, Jialin Lumbres, Jennifer Hedglen, Alexander Schatz, Lauren McLeod, Avalon Doty, Isabella Foster, Warren B. Roberts, Roswell Twitchell, Katie |
| author_facet | Long, Joseph D. Males, Jared R. Close, Laird M. Guyon, Olivier Haffert, Sebastiaan Y. Weinberger, Alycia J. Kueny, Jay Van Gorkom, Kyle McEwen, Eden Pearce, Logan Kautz, Maggie Li, Jialin Lumbres, Jennifer Hedglen, Alexander Schatz, Lauren McLeod, Avalon Doty, Isabella Foster, Warren B. Roberts, Roswell Twitchell, Katie |
| contents | 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 will eventually allow us to probe smaller star-planet separations at greater sensitivities, which will be needed for rocky planet imaging. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_13008 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | More data than you want, less data than you need: machine learning approaches to starlight subtraction with MagAO-X Long, Joseph D. Males, Jared R. Close, Laird M. Guyon, Olivier Haffert, Sebastiaan Y. Weinberger, Alycia J. Kueny, Jay Van Gorkom, Kyle McEwen, Eden Pearce, Logan Kautz, Maggie Li, Jialin Lumbres, Jennifer Hedglen, Alexander Schatz, Lauren McLeod, Avalon Doty, Isabella Foster, Warren B. Roberts, Roswell Twitchell, Katie Instrumentation and Methods for Astrophysics 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 will eventually allow us to probe smaller star-planet separations at greater sensitivities, which will be needed for rocky planet imaging. |
| title | More data than you want, less data than you need: machine learning approaches to starlight subtraction with MagAO-X |
| topic | Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2407.13008 |