Males, Jared R.
Morzinski, Katie M.
Close, Laird M.
Univ Arizona, Dept Astron
Univ Arizona, Inst BIO5
MetadataShow full item record
PublisherSPIE-INT SOC OPTICAL ENGINEERING
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/10.1117/12.2234095
Rights© 2016 SPIE
Collection InformationThis 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 firstname.lastname@example.org.
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.
VersionFinal published version