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Title: DETECTING UNSPECIFIED STRUCTURE IN LOW-COUNT IMAGES

Unexpected structure in images of astronomical sources often presents itself upon visual inspection of the image, but such apparent structure may either correspond to true features in the source or be due to noise in the data. This paper presents a method for testing whether inferred structure in an image with Poisson noise represents a significant departure from a baseline (null) model of the image. To infer image structure, we conduct a Bayesian analysis of a full model that uses a multiscale component to allow flexible departures from the posited null model. As a test statistic, we use a tail probability of the posterior distribution under the full model. This choice of test statistic allows us to estimate a computationally efficient upper bound on a p-value that enables us to draw strong conclusions even when there are limited computational resources that can be devoted to simulations under the null model. We demonstrate the statistical performance of our method on simulated images. Applying our method to an X-ray image of the quasar 0730+257, we find significant evidence against the null model of a single point source and uniform background, lending support to the claim of an X-ray jet.
Authors:
 [1] ;  [2] ; ;  [3]
  1. Department of Statistics, The Wharton School, University of Pennsylvania, 400 Jon M. Huntsman Hall, 3730 Walnut Street, Philadelphia, PA 19104-6340 (United States)
  2. Statistics Section, Imperial College London Huxley Building, South Kensington Campus, London SW7 2AZ (United Kingdom)
  3. Smithsonian Astrophysical Observatory, 60 Garden Street, Cambridge, MA 02138 (United States)
Publication Date:
OSTI Identifier:
22518720
Resource Type:
Journal Article
Resource Relation:
Journal Name: Astrophysical Journal; Journal Volume: 813; 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; DATA ANALYSIS; GALAXIES; IMAGE PROCESSING; IMAGES; JETS; NOISE; PERFORMANCE; POINT SOURCES; PROBABILITY; QUASARS; X RADIATION