Bayesian modeling of source confusion in LISA data

Journal Article · Fri Jul 15 04:00:00 EDT 2005 · Physical Review. D, Particles Fields

DOI:https://doi.org/10.1103/PhysRevD.72.022001· OSTI ID:20711097

Umstaetter, Richard; Meyer, Renate ^[1]; Christensen, Nelson; Vigeland, Sarah ^[2]; Hendry, Martin; Simha, Vimal; Veitch, John; Woan, Graham ^[3]

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1. Department of Statistics, University of Auckland, Auckland (New Zealand)
2. Physics and Astronomy, Carleton College, Northfield, Minnesota 55057 (United States)
3. Department of Physics and Astronomy, University of Glasgow, Glasgow G12 8QQ (United Kingdom)

One of the greatest data analysis challenges for the Laser Interferometer Space Antenna (LISA) is the need to account for a large number of gravitational wave signals from compact binary systems expected to be present in the data. We introduce the basis of a Bayesian method that we believe can address this challenge and demonstrate its effectiveness on a simplified problem involving 100 synthetic sinusoidal signals in noise. We use a reversible jump Markov chain Monte Carlo technique to infer simultaneously the number of signals present, the parameters of each identified signal, and the noise level. Our approach therefore tackles the detection and parameter estimation problems simultaneously, without the need to evaluate formal model selection criteria, such as the Akaike Information Criterion or explicit Bayes factors. The method does not require a stopping criterion to determine the number of signals and produces results which compare very favorably with classical spectral techniques.

• OSTI ID: 20711097
• Journal Information: Physical Review. D, Particles Fields, Journal Name: Physical Review. D, Particles Fields Journal Issue: 2 Vol. 72; ISSN PRVDAQ; ISSN 0556-2821
• Country of Publication: United States
• Language: English

Related Subjects

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS
ABSORPTION
ANTENNAS
COMPARATIVE EVALUATIONS
DATA ANALYSIS
GRAVITATIONAL WAVE DETECTORS
GRAVITATIONAL WAVES
INTERFEROMETERS
INTERFEROMETRY
LASERS
MARKOV PROCESS
MONTE CARLO METHOD
NOISE
SIGNALS
SIMULATION
VISIBLE RADIATION