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Journal Article

Targeted search for the stochastic gravitational-wave background from the galactic millisecond pulsar population


Matas,  Andrew
Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society;

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Agarwal, D., Suresh, J., Mandic, V., Matas, A., & Regimbau, T. (2022). Targeted search for the stochastic gravitational-wave background from the galactic millisecond pulsar population. Physical Review D, 106(4): 043019.

Cite as: https://hdl.handle.net/21.11116/0000-000A-589A-6
The millisecond pulsars, old-recycled objects spinning with high frequency
$\mathcal{O}$(kHz) sustaining the deformation from their spherical shape, may
emit gravitational-waves (GW). These are one of the potential candidates
contributing to the anisotropic stochastic gravitational-wave background (SGWB)
observable in the ground-based GW detectors. Here, we present the results from
a likelihood-based targeted search for the SGWB due to millisecond pulsars in
the Milky Way, by analyzing the data from the first three observing runs of
Advanced LIGO and Advanced Virgo detector. We assume that the shape of SGWB
power spectra and the sky distribution is known a priori from the population
synthesis model. The information of the ensemble source properties, i.e., the
in-band number of pulsars, $N_{obs}$ and the averaged ellipticity,
$\mu_\epsilon$ is encoded in the maximum likelihood statistic. We do not find
significant evidence for the SGWB signal from the considered source population.
The best Bayesian upper limit with $95\%$ confidence for the parameters are
$N_{obs}\leq8.8\times10^{4}$ and $\mu_\epsilon\leq1.1\times10^{-7}$, which is
comparable to the bounds on mean ellipticity with the GW observations of the
individual pulsars. Finally, we show that for the plausible case of
$N_{obs}=40,000$, with the one year of observations, the one-sigma sensitivity
on $\mu_\epsilon$ might reach $10^{-8}$ and $2.7\times10^{-9}$ for the
second-generation detector network having A+ sensitivity and third-generation
detector network respectively.