XPipeline: Starlight subtraction at scale for MagAO-X

Long, J.D.; Males, J.R.; Haffert, S.Y.; Close, L.M.; Morzinski, K.M.; van Gorkom, K.; Lumbres, J.; Foster, W.; Hedglen, A.; Kautz, M.; Rodack, A.; Schatz, L.; Miller, K.; Doelman, D.; Bos, S.; Kenworthy, M.A.; Snik, F.; Otten, G.P.P.L.

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Affiliation

Steward Observatory, University of Arizona
Wyant College of Optical Sciences, University of Arizona

Issue Date

2022-08-29

Citation

Joseph D. Long, Jared R. Males, Sebastiaan Y. Haffert, Laird M. Close, Katie M. Morzinski, Kyle Van Gorkom, Jennifer Lumbres, Warren Foster, Alexander Hedglen, Maggie Kautz, Alex Rodack, Lauren Schatz, Kelsey Miller, David Doelman, Steven P. Bos, Matthew A. Kenworthy, Frans Snik, Gilles P. P. L. Otten, "XPipeline: starlight subtraction at scale for MagAO-X," Proc. SPIE 12185, Adaptive Optics Systems VIII, 121853P (29 August 2022); https://doi.org/10.1117/12.2628975

Journal

Proceedings of SPIE - The International Society for Optical Engineering

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This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.

Abstract

MagAO-X is an extreme adaptive optics (ExAO) instrument for the Magellan Clay 6.5-meter telescope at Las Campanas Observatory in Chile. Its high spatial and temporal resolution can produce data rates of 1 TB/hr or more, including all AO system telemetry and science images. We describe the tools and architecture we use for commanding, telemetry, and science data transmission and storage. The high data volumes require a distributed approach to data processing, and we have developed a pipeline that can scale from a single laptop to dozens of HPC nodes. The same codebase can then be used for both quick-look functionality at the telescope and for post-processing. We present the software and infrastructure we have developed for ExAO data post-processing, and illustrate their use with recently acquired direct-imaging data. © 2022 SPIE.

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Immediate access

ISSN

0277-786X

ISBN

978-151065351-1

DOI

10.1117/12.2628975

Version

Final Published Version

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UA Faculty Publications