Subtraction-noise projection in gravitational-wave detector networks

TL;DR

This paper demonstrates a noise projection method to effectively remove subtraction noise in gravitational-wave data, enhancing the detection of weak stochastic backgrounds amidst strong foreground signals.

Contribution

The paper introduces a practical implementation of subtraction-noise projection within a simulated gravitational-wave data analysis pipeline for the BBO mission.

Findings

Subtraction-noise projection successfully reduces residual noise.

BBO can detect backgrounds with fractional energy densities below 10^{-16}.

Simulation confirms the effectiveness of the proposed method.

Abstract

In this paper, we present a successful implementation of a subtraction-noise projection method into a simple, simulated data analysis pipeline of a gravitational-wave search. We investigate the problem to reveal a weak stochastic background signal which is covered by a strong foreground of compact-binary coalescences. The foreground which is estimated by matched filters, has to be subtracted from the data. Even an optimal analysis of foreground signals will leave subtraction noise due to estimation errors of template parameters which may corrupt the measurement of the background signal. The subtraction noise can be removed by a noise projection. We apply our analysis pipeline to the proposed future-generation space-borne Big Bang Observer (BBO) mission which seeks for a stochastic background of primordial GWs in the frequency range $\sim 0.1-1 $Hz covered by a foreground of black-hole and neutron-star binaries. Our analysis is based on a simulation code which provides a dynamical model of a time-delay interferometer (TDI) network. It generates the data as time series and incorporates the analysis pipeline together with the noise projection. Our results confirm previous ad hoc predictions which say that BBO will be sensitive to backgrounds with fractional energy densities below $\Omega=10^{-16}$