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Title: Faint Object Detection in Multi-Epoch Observations via Catalog Data Fusion

Abstract

Astronomy in the time-domain era faces several new challenges. One of them is the efficient use of observations obtained at multiple epochs. The work presented here addresses faint object detection and describes an incremental strategy for separating real objects from artifacts in ongoing surveys. The idea is to produce low-threshold single-epoch catalogs and to accumulate information across epochs. This is in contrast to more conventional strategies based on co-added or stacked images. We adopt a Bayesian approach, addressing object detection by calculating the marginal likelihoods for hypotheses asserting that there is no object or one object in a small image patch containing at most one cataloged source at each epoch. The object-present hypothesis interprets the sources in a patch at different epochs as arising from a genuine object; the no-object hypothesis interprets candidate sources as spurious, arising from noise peaks. We study the detection probability for constant-flux objects in a Gaussian noise setting, comparing results based on single and stacked exposures to results based on a series of single-epoch catalog summaries. Our procedure amounts to generalized cross-matching: it is the product of a factor accounting for the matching of the estimated fluxes of the candidate sources and a factor accountingmore » for the matching of their estimated directions. We find that probabilistic fusion of multi-epoch catalogs can detect sources with similar sensitivity and selectivity compared to stacking. The probabilistic cross-matching framework underlying our approach plays an important role in maintaining detection sensitivity and points toward generalizations that could accommodate variability and complex object structure.« less

Authors:
;  [1];  [2]
  1. Department of Physics and Astronomy, The Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218 (United States)
  2. Cornell Center for Astrophysics and Planetary Science, Cornell University, Ithaca, NY 14853 (United States)
Publication Date:
OSTI Identifier:
22661241
Resource Type:
Journal Article
Resource Relation:
Journal Name: Astrophysical Journal; Journal Volume: 838; 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; ACCOUNTING; ASTRONOMY; CATALOGS; COMPARATIVE EVALUATIONS; DETECTION; GALAXIES; HYPOTHESIS; NOISE; PHOTOMETRY; PROBABILISTIC ESTIMATION; PROBABILITY; SENSITIVITY

Citation Formats

Budavári, Tamás, Szalay, Alexander S., and Loredo, Thomas J. Faint Object Detection in Multi-Epoch Observations via Catalog Data Fusion. United States: N. p., 2017. Web. doi:10.3847/1538-4357/AA6335.
Budavári, Tamás, Szalay, Alexander S., & Loredo, Thomas J. Faint Object Detection in Multi-Epoch Observations via Catalog Data Fusion. United States. doi:10.3847/1538-4357/AA6335.
Budavári, Tamás, Szalay, Alexander S., and Loredo, Thomas J. Mon . "Faint Object Detection in Multi-Epoch Observations via Catalog Data Fusion". United States. doi:10.3847/1538-4357/AA6335.
@article{osti_22661241,
title = {Faint Object Detection in Multi-Epoch Observations via Catalog Data Fusion},
author = {Budavári, Tamás and Szalay, Alexander S. and Loredo, Thomas J.},
abstractNote = {Astronomy in the time-domain era faces several new challenges. One of them is the efficient use of observations obtained at multiple epochs. The work presented here addresses faint object detection and describes an incremental strategy for separating real objects from artifacts in ongoing surveys. The idea is to produce low-threshold single-epoch catalogs and to accumulate information across epochs. This is in contrast to more conventional strategies based on co-added or stacked images. We adopt a Bayesian approach, addressing object detection by calculating the marginal likelihoods for hypotheses asserting that there is no object or one object in a small image patch containing at most one cataloged source at each epoch. The object-present hypothesis interprets the sources in a patch at different epochs as arising from a genuine object; the no-object hypothesis interprets candidate sources as spurious, arising from noise peaks. We study the detection probability for constant-flux objects in a Gaussian noise setting, comparing results based on single and stacked exposures to results based on a series of single-epoch catalog summaries. Our procedure amounts to generalized cross-matching: it is the product of a factor accounting for the matching of the estimated fluxes of the candidate sources and a factor accounting for the matching of their estimated directions. We find that probabilistic fusion of multi-epoch catalogs can detect sources with similar sensitivity and selectivity compared to stacking. The probabilistic cross-matching framework underlying our approach plays an important role in maintaining detection sensitivity and points toward generalizations that could accommodate variability and complex object structure.},
doi = {10.3847/1538-4357/AA6335},
journal = {Astrophysical Journal},
number = 1,
volume = 838,
place = {United States},
year = {Mon Mar 20 00:00:00 EDT 2017},
month = {Mon Mar 20 00:00:00 EDT 2017}
}
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