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Title: Solution to the galactic foreground problem for LISA

Abstract

Low frequency gravitational wave detectors, such as the Laser Interferometer Space Antenna (LISA), will have to contend with large foregrounds produced by millions of compact galactic binaries in our galaxy. While these galactic signals are interesting in their own right, the unresolved component can obscure other sources. The science yield for the LISA mission can be improved if the brighter and more isolated foreground sources can be identified and regressed from the data. Since the signals overlap with one another, we are faced with a 'cocktail party' problem of picking out individual conversations in a crowded room. Here we present and implement an end-to-end solution to the galactic foreground problem that is able to resolve tens of thousands of sources from across the LISA band. Our algorithm employs a variant of the Markov chain Monte Carlo (MCMC) method, which we call the blocked annealed Metropolis-Hastings (BAM) algorithm. Following a description of the algorithm and its implementation, we give several examples ranging from searches for a single source to searches for hundreds of overlapping sources. Our examples include data sets from the first round of mock LISA data challenges.

Authors:
 [1];  [2];  [1]
  1. Department of Physics, Montana State University, Bozeman, Montana 59717 (United States)
  2. (United States)
Publication Date:
OSTI Identifier:
21011040
Resource Type:
Journal Article
Resource Relation:
Journal Name: Physical Review. D, Particles Fields; Journal Volume: 75; Journal Issue: 4; Other Information: DOI: 10.1103/PhysRevD.75.043008; (c) 2007 The American Physical Society; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; ALGORITHMS; ANTENNAS; COSMOLOGY; GRAVITATIONAL WAVE DETECTORS; INTERFEROMETERS; LASERS; MARKOV PROCESS; MATHEMATICAL SOLUTIONS; MONTE CARLO METHOD; NONLUMINOUS MATTER; SIGNALS

Citation Formats

Crowder, Jeff, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California 91109, and Cornish, Neil J. Solution to the galactic foreground problem for LISA. United States: N. p., 2007. Web. doi:10.1103/PHYSREVD.75.043008.
Crowder, Jeff, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California 91109, & Cornish, Neil J. Solution to the galactic foreground problem for LISA. United States. doi:10.1103/PHYSREVD.75.043008.
Crowder, Jeff, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California 91109, and Cornish, Neil J. Thu . "Solution to the galactic foreground problem for LISA". United States. doi:10.1103/PHYSREVD.75.043008.
@article{osti_21011040,
title = {Solution to the galactic foreground problem for LISA},
author = {Crowder, Jeff and Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California 91109 and Cornish, Neil J.},
abstractNote = {Low frequency gravitational wave detectors, such as the Laser Interferometer Space Antenna (LISA), will have to contend with large foregrounds produced by millions of compact galactic binaries in our galaxy. While these galactic signals are interesting in their own right, the unresolved component can obscure other sources. The science yield for the LISA mission can be improved if the brighter and more isolated foreground sources can be identified and regressed from the data. Since the signals overlap with one another, we are faced with a 'cocktail party' problem of picking out individual conversations in a crowded room. Here we present and implement an end-to-end solution to the galactic foreground problem that is able to resolve tens of thousands of sources from across the LISA band. Our algorithm employs a variant of the Markov chain Monte Carlo (MCMC) method, which we call the blocked annealed Metropolis-Hastings (BAM) algorithm. Following a description of the algorithm and its implementation, we give several examples ranging from searches for a single source to searches for hundreds of overlapping sources. Our examples include data sets from the first round of mock LISA data challenges.},
doi = {10.1103/PHYSREVD.75.043008},
journal = {Physical Review. D, Particles Fields},
number = 4,
volume = 75,
place = {United States},
year = {Thu Feb 15 00:00:00 EST 2007},
month = {Thu Feb 15 00:00:00 EST 2007}
}
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