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dc.contributor.authorHaug-Baltzell, Asher
dc.contributor.authorMales, Jared R.
dc.contributor.authorMorzinski, Katie M.
dc.contributor.authorWu, Ya-Lin
dc.contributor.authorMerchant, Nirav
dc.contributor.authorLyons, Eric
dc.contributor.authorClose, Laird M.
dc.date.accessioned2017-02-15T17:23:27Z
dc.date.available2017-02-15T17:23:27Z
dc.date.issued2016-08-08
dc.identifier.citationAsher Haug-Baltzell ; Jared R. Males ; Katie M. Morzinski ; Ya-Lin Wu ; Nirav Merchant ; Eric Lyons and Laird M. Close " High-contrast imaging in the cloud with klipReduce and Findr ", Proc. SPIE 9913, Software and Cyberinfrastructure for Astronomy IV, 99130F (August 8, 2016); doi:10.1117/12.2234095; http://dx.doi.org/10.1117/12.2234095en
dc.identifier.issn0277-786X
dc.identifier.doi10.1117/12.2234095
dc.identifier.urihttp://hdl.handle.net/10150/622548
dc.description.abstractAstronomical 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-Loeve 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.
dc.language.isoenen
dc.publisherSPIE-INT SOC OPTICAL ENGINEERINGen
dc.relation.urlhttp://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2234095en
dc.rights© 2016 SPIE.en
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectCyberinfrastructureen
dc.subjectcloud computingen
dc.subjectexoplanet imagingen
dc.subjectadaptive opticsen
dc.subjectKLIPen
dc.subjectPSF subtractionen
dc.subjectVisAOen
dc.titleHigh-contrast imaging in the cloud with klipReduce and Findren
dc.typeArticleen
dc.contributor.departmentUniv Arizonaen
dc.contributor.departmentUniv Arizona, Dept Astronen
dc.contributor.departmentUniv Arizona, Inst BIO5en
dc.identifier.journalSOFTWARE AND CYBERINFRASTRUCTURE FOR ASTRONOMY IVen
dc.description.collectioninformationThis 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.en
dc.eprint.versionFinal published versionen
dc.contributor.institutionUniv. of Arizona (United States)
dc.contributor.institutionUniv. of Arizona (United States)
dc.contributor.institutionUniv. of Arizona (United States)
dc.contributor.institutionUniv. of Arizona (United States)
dc.contributor.institutionUniv. of Arizona (United States)
dc.contributor.institutionUniv. of Arizona (United States)
dc.contributor.institutionUniv. of Arizona (United States)
refterms.dateFOA2018-09-11T17:38:10Z
html.description.abstractAstronomical 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-Loeve 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.


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