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Proceedings Paper

High-contrast imaging in the cloud with klipReduce and Findr
Author(s): Asher Haug-Baltzell; Jared R. Males; Katie M. Morzinski; Ya-Lin Wu; Nirav Merchant; Eric Lyons; Laird M. Close
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Paper Abstract

Astronomical data sets are growing ever larger, and the area of high contrast imaging of exoplanets is no exception. With the advent of fast, low-noise detectors operating at 10 to 1000 Hz, huge numbers of images can be taken during a single hours-long observation. High frame rates offer several advantages, such as improved registration, frame selection, and improved speckle calibration. However, advanced image processing algorithms are computationally challenging to apply. Here we describe a parallelized, cloud-based data reduction system developed for the Magellan Adaptive Optics VisAO camera, which is capable of rapidly exploring tens of thousands of parameter sets affecting the Karhunen-Loève image processing (KLIP) algorithm to produce high-quality direct images of exoplanets. We demonstrate these capabilities with a visible wavelength high contrast data set of a hydrogen-accreting brown dwarf companion.

Paper Details

Date Published: 8 August 2016
PDF: 7 pages
Proc. SPIE 9913, Software and Cyberinfrastructure for Astronomy IV, 99130F (8 August 2016); doi: 10.1117/12.2234095
Show Author Affiliations
Asher Haug-Baltzell, Univ. of Arizona (United States)
Jared R. Males, Univ. of Arizona (United States)
Katie M. Morzinski, Univ. of Arizona (United States)
Ya-Lin Wu, Univ. of Arizona (United States)
Nirav Merchant, Univ. of Arizona (United States)
Eric Lyons, Univ. of Arizona (United States)
Laird M. Close, Univ. of Arizona (United States)

Published in SPIE Proceedings Vol. 9913:
Software and Cyberinfrastructure for Astronomy IV
Gianluca Chiozzi; Juan C. Guzman, Editor(s)

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