de.mpg.escidoc.pubman.appbase.FacesBean
English

# Item

ITEM ACTIONSEXPORT

Released

Journal Article

#### Understanding and analysing time-correlated stochastic signals in pulsar timing

##### MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons86728

van Haasteren,  Rutger
Observational Relativity and Cosmology, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society;

##### Locator
There are no locators available
##### Fulltext (public)

1202.5932
(Preprint), 862KB

MNRAS428_1147.full.pdf
(Any fulltext), 2MB

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

van Haasteren, R., & Levin, Y. (2013). Understanding and analysing time-correlated stochastic signals in pulsar timing. Monthly Notices of the Royal Astronomical Society, 428(2), 1147-1159. doi:10.1093/mnras/sts097.

Cite as: http://hdl.handle.net/11858/00-001M-0000-000E-FCAB-D
##### Abstract
Although it is widely understood that pulsar timing observations generally contain time-correlated stochastic signals (TCSSs; red timing noise is of this type), most data analysis techniques that have been developed make an assumption that the stochastic uncertainties in the data are uncorrelated, i.e. "white". Recent work has pointed out that this can introduce severe bias in determination of timing-model parameters, and that better analysis methods should be used. This paper presents a detailed investigation of timing-model fitting in the presence of TCSSs, and gives closed expressions for the post-fit signals in the data. This results in a Bayesian technique to obtain timing-model parameter estimates in the presence of TCSSs, as well as computationally more efficient expressions of their marginalised posterior distribution. A new method to analyse hundreds of mock dataset realisations simultaneously without significant computational overhead is presented, as well as a statistically rigorous method to check the internal consistency of the results. As a by-product of the analysis, closed expressions of the rms introduced by a stochastic background of gravitational-waves in timing-residuals are obtained. Using $T$ as the length of the dataset, and $h_c(1\rm{yr}^{-1})$ as the characteristic strain, this is: $\sigma_{\rm GWB}^2 = h_{c}(1\rm{yr}^{-1})^2 (9\sqrt[3]{2\pi^4}\Gamma(-10/3) / 8008) \rm{yr}^{-4/3} T^{10/3}$.