4.7 Article

Spectral separation of the stochastic gravitational-wave background for LISA: Observing both cosmological and astrophysical backgrounds

Journal

PHYSICAL REVIEW D
Volume 103, Issue 10, Pages -

Publisher

AMER PHYSICAL SOC
DOI: 10.1103/PhysRevD.103.103529

Keywords

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Funding

  1. Centre national d'Etudes spatiales (CNES)
  2. NASA LISA Preparatory Science [80NSSC19K0320]
  3. James Cook Fellowship from Government
  4. DFG [KI 1443/3-2]

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This study aims to observe a stochastic gravitational-wave background with LISA, emphasizing the importance of spectral separability between cosmological and astrophysical backgrounds. Using adaptive Markov chain Monte Carlo methods, the detectable limits for future LISA measurements of SGWB are estimated, along with calculating the Cramer-Rao lower bound. Based on the expected astrophysical background level, LISA can detect a specific cosmological SGWB energy density after 4 years of observation.
With the goal of observing a stochastic gravitational-wave background (SGWB) with LISA, the spectral separability of the cosmological and astrophysical backgrounds is important to estimate. We attempt to determine the level with which a cosmological background can be observed given the predicted astrophysical background level. We predict detectable limits for the future LISA measurement of the SGWB. Adaptive Markov chain Monte Carlo methods are used to produce estimates with the simulated data from the LISA Data Challenge. We also calculate the Cramer-Rao lower bound on the variance of the SGWB parameter estimates based on the inverse Fisher information using the Whittle likelihood. The estimation of the parameters is done with the three LISA channels A, E, and T. We simultaneously estimate the noise using a LISA noise model. Assuming the expected astrophysical background around Omega(GW,astro)(25 Hz) = 0.355 -> 35.5 x 10(-9), a cosmological SGWB normalized energy density of around Omega(GW, Cosmo) approximate to 1 x 10(-12) to 1 x 10(-13) can be detected by LISA after 4 years of observation.

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