de.mpg.escidoc.pubman.appbase.FacesBean
English
 
Help Guide Disclaimer Contact us Login
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Resolving multiple supermassive black hole binaries with pulsar timing arrays II: genetic algorithm implementation

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons20664

Petiteau,  Antoine
Astrophysical Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons20655

Babak,  Stanislav
Astrophysical Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons2713

Sesana,  Alberto
Astrophysical Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;

Locator
There are no locators available
Fulltext (public)

1210.2396.pdf
(Preprint), 2MB

PRD87_064036.pdf
(Any fulltext), 2MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Petiteau, A., Babak, S., Sesana, A., & de Araujo, M. (2013). Resolving multiple supermassive black hole binaries with pulsar timing arrays II: genetic algorithm implementation. Physical Review D, 87: 064036. doi:10.1103/PhysRevD.87.064036.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0015-1927-E
Abstract
Pulsar timing arrays (PTAs) might detect gravitational waves (GWs) from massive black hole (MBH) binaries within this decade. The signal is expected to be an incoherent superposition of several nearly-monochromatic waves of different strength. The brightest sources might be individually resolved, and the overall deconvolved, at least partially, in its individual components. In this paper we extend the maximum-likelihood based method developed in Babak & Sesana 2012, to search for individual MBH binaries in PTA data. We model the signal as a collection of circular monochromatic binaries, each characterized by three free parameters: two angles defining the sky location, and the frequency. We marginalize over all other source parameters and we apply an efficient multi-search genetic algorithm to maximize the likelihood function and look for sources in synthetic datasets. On datasets characterized by white Gaussian noise plus few injected sources with signal-to-noise ratio (SNR) in the range 10-60, our search algorithm performs well, recovering all the injections with no false positives. Individual source SNRs are estimated within few % of the injected values, sky locations are recovered within few degrees, and frequencies are determined with sub-Fourier bin precision.