Published October 21, 2009 | Version v1
Journal article

Targeted search for continuous gravitational waves: Bayesian versus maximum-likelihood statistics

  • 1. Albert-Einstein-Institut, Callinstr. 38, 30167 Hannover (Germany)
  • 2. Albert-Einstein-Institut, Am Muehlenberg 1, 14476 Potsdam (Germany)

Description

We investigate the Bayesian framework for detection of continuous gravitational waves (GWs) in the context of targeted searches, where the phase evolution of the GW signal is assumed to be known, while the four amplitude parameters are unknown. We show that the orthodox maximum-likelihood statistic (known as F-statistic) can be rediscovered as a Bayes factor with an unphysical prior in amplitude parameter space. We introduce an alternative detection statistic ('B-statistic') using the Bayes factor with a more natural amplitude prior, namely an isotropic probability distribution for the orientation of GW sources. Monte Carlo simulations of targeted searches show that the resulting Bayesian B-statistic is more powerful in the Neyman-Pearson sense (i.e., has a higher expected detection probability at equal false-alarm probability) than the frequentist F-statistic.

Availability note (English)

Available from http://dx.doi.org/10.1088/0264-9381/26/20/204013

Additional details

Identifiers

DOI
10.1088/0264-9381/26/20/204013;
PII
S0264-9381(09)18788-9;

Publishing Information

Journal Title
Classical and Quantum Gravity
Journal Volume
26
Journal Issue
20
Journal Page Range
[12 p.]
ISSN
0264-9381
CODEN
CQGRDG

Conference

Title
13. gravitational wave data analysis workshop
Acronym
GWDAW13
Dates
19-22 Jan 2009
Place
San Juan (Puerto Rico)

INIS

Country of Publication
United Kingdom
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
41106603
Subject category
S71: CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS;
Resource subtype / Literary indicator
Conference
Descriptors DEI
AMPLITUDES; COMPUTERIZED SIMULATION; DETECTION; EVOLUTION; GRAVITATIONAL WAVES; MAXIMUM-LIKELIHOOD FIT; MONTE CARLO METHOD; ORIENTATION; PROBABILITY; SIGNALS; SPACE; STATISTICS
Descriptors DEC
CALCULATION METHODS; MATHEMATICAL SOLUTIONS; MATHEMATICS; NUMERICAL SOLUTION; SIMULATION