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Title: Optimized principal component analysis on coronagraphic images of the fomalhaut system

We present the results of a study to optimize the principal component analysis (PCA) algorithm for planet detection, a new algorithm complementing angular differential imaging and locally optimized combination of images (LOCI) for increasing the contrast achievable next to a bright star. The stellar point spread function (PSF) is constructed by removing linear combinations of principal components, allowing the flux from an extrasolar planet to shine through. The number of principal components used determines how well the stellar PSF is globally modeled. Using more principal components may decrease the number of speckles in the final image, but also increases the background noise. We apply PCA to Fomalhaut Very Large Telescope NaCo images acquired at 4.05 μm with an apodized phase plate. We do not detect any companions, with a model dependent upper mass limit of 13-18 M {sub Jup} from 4-10 AU. PCA achieves greater sensitivity than the LOCI algorithm for the Fomalhaut coronagraphic data by up to 1 mag. We make several adaptations to the PCA code and determine which of these prove the most effective at maximizing the signal-to-noise from a planet very close to its parent star. We demonstrate that optimizing the number of principal components usedmore » in PCA proves most effective for pulling out a planet signal.« less
Authors:
;  [1] ; ;  [2]
  1. Sterrewacht Leiden, P.O. Box 9513, Niels Bohrweg 2, 2300-RA Leiden (Netherlands)
  2. Institute for Astronomy, ETH Zurich, Wolfgang-Pauli-Strasse 27, 8093-CH Zurich (Switzerland)
Publication Date:
OSTI Identifier:
22348374
Resource Type:
Journal Article
Resource Relation:
Journal Name: Astrophysical Journal; Journal Volume: 780; Journal Issue: 1; Other Information: Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
79 ASTROPHYSICS, COSMOLOGY AND ASTRONOMY; ALGORITHMS; BACKGROUND NOISE; DATA ANALYSIS; DETECTION; IMAGE PROCESSING; MASS; OPTIMIZATION; PLANETS; POLAR-CAP ABSORPTION; SATELLITES; SENSITIVITY; STARS; TELESCOPES