MG14 - Talk detail

Back to previous page

 Participant

Institution

Session

Accepted

Yes

Order

Time

Talk

Title

Abstract

Gravitational-wave (GW) detection is expected to be made within a decade. The most promising sources are compact binary coalescences such as black hole – neutron star (BH-NS) and BH-BH binary mergers. Parameter estimation (PE) provides a statistical framework to estimate physical properties of GW sources based on the Bayesian inference. Using the Markov Chain Monte Carlo (MCMC) sampling algorithm and a post-Newtonian (PN or pN hereafter) waveform model, we perform MCMC PE for artificial GW signals. We use TaylorF2Amp, which is a pN waveform model in a frequency-domain. TaylorF2Amp includes up to 2.5pN (in amplitude) and 3.5pN (in phase) corrections. No spin effects are taken into account. In this talk, we present results from our MCMC PE study: (a) the recovered 1D posterior probability density functions of a few important parameters (e.g. mass, distance, orbital phase, etc), and (b) 2D correlations between selected parameters. By comparing the computation time and posteriors, we conclude that 1.5pN amplitude corrections are necessary in order to constrain the orbital phase and polarization angle of the binary. However, higher corrections for GW amplitude would not improve the PE accuracy significantly. This work is done in collaboration with the LIGO (Laser Interferometer Gravitational-wave Observatory) Scientific Collaboration.

Pdf file

Back to previous page