Uploaded August 2020 | Updated September 2026, 2 hours ago
Spectral separation of the Stochastic Gravitational wave Background
Guillaume Boileau,Nelson Christensen, Renate Meyer
With the goal of attempting to observe a stochastic gravitational wave background (SGWB) with LISA, the study of 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 measurement of the SGWB. Adaptive Markov chain Monte-Carlo (Adaptive-McMC) are used to produce estimates with the simulated data from the LISA Data challenge (LDC). We also calculate the Cramer-Rao lower bound on the variance of the SGWB parameter uncertainties based on the inverse Fisher Information using the Whittle Likelihood. The estimation of the parameters is done with the 3 channels A,E,T. We simultaneously estimate the noise using a LISA noise model.
Spectral separation of the Stochastic Gravitational wave Background
Guillaume Boileau,Nelson Christensen, Renate Meyer
With the goal of attempting to observe a stochastic gravitational wave background (SGWB) with LISA, the study of 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 measurement of the SGWB. Adaptive Markov chain Monte-Carlo (Adaptive-McMC) are used to produce estimates with the simulated data from the LISA Data challenge (LDC). We also calculate the Cramer-Rao lower bound on the variance of the SGWB parameter uncertainties based on the inverse Fisher Information using the Whittle Likelihood. The estimation of the parameters is done with the 3 channels A,E,T. We simultaneously estimate the noise using a LISA noise model.










