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-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.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/622548 |
| Date | 08 August 2016 |
| Creators | Haug-Baltzell, Asher, Males, Jared R., Morzinski, Katie M., Wu, Ya-Lin, Merchant, Nirav, Lyons, Eric, Close, Laird M. |
| Contributors | Univ Arizona, Univ Arizona, Dept Astron, Univ Arizona, Inst BIO5, Univ. of Arizona (United States), Univ. of Arizona (United States), Univ. of Arizona (United States), Univ. of Arizona (United States), Univ. of Arizona (United States), Univ. of Arizona (United States), Univ. of Arizona (United States) |
| Publisher | SPIE-INT SOC OPTICAL ENGINEERING |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Article |
| Rights | © 2016 SPIE |
| Relation | http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2234095 |
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