Principal Component Analysis as a Tool for Characterizing Black Hole Images and Variability
AffiliationUniv Arizona, Steward Observ
Univ Arizona, Dept Astron
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PublisherIOP PUBLISHING LTD
CitationLia Medeiros et al 2018 ApJ 864 7
Rights© 2018. The American Astronomical Society. All rights reserved.
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AbstractWe explore the use of principal component analysis (PCA) to characterize high-fidelity simulations and interferometric observations of the millimeter emission that originates near the horizons of accreting black holes. We show mathematically that the Fourier transforms of eigenimages derived from PCA applied to an ensemble of images in the spatial domain are identical to the eigenvectors of PCA applied to the ensemble of the Fourier transforms of the images, which suggests that this approach may be applied to modeling the sparse interferometric Fourier-visibilities produced by an array such as the Event Horizon Telescope. We also show that the simulations in the spatial domain can themselves be compactly represented with a PCA-derived basis of eigenimages, which allows for detailed comparisons to be made between variable observations and time-dependent models, as well as for detection of outliers or rare events within a time series of images. Furthermore, we demonstrate that the spectrum of PCA eigenvalues is a diagnostic of the power spectrum of the structure and, hence, of the underlying physical processes in the simulated and observed images.
VersionFinal published version
SponsorsNSF GRFP grant [DGE 1144085]; NSF PIRE grant ; NSF [AST-1715061]; Chandra Award [TM8-19008X]; NSF award