Discriminating between a Stochastic Gravitational Wave Background and Instrument Noise

TL;DR

This paper presents methods to distinguish a stochastic gravitational wave background from instrument noise using models, null channels, and Bayesian analysis, enabling detection with a single detector like LISA.

Contribution

It introduces a new analysis framework combining transfer function models, null channels, and Bayesian inference for detecting gravitational wave backgrounds with one detector.

Findings

LISA could detect signals with energy density as low as 6 x 10^{-13} in one month

New null channel combination maintains insensitivity for unequal arm lengths

End-to-end Bayesian pipeline effectively characterizes stochastic backgrounds

Abstract

The detection of a stochastic background of gravitational waves could significantly impact our understanding of the physical processes that shaped the early Universe. The challenge lies in separating the cosmological signal from other stochastic processes such as instrument noise and astrophysical foregrounds. One approach is to build two or more detectors and cross correlate their output, thereby enhancing the common gravitational wave signal relative to the uncorrelated instrument noise. When only one detector is available, as will likely be the case with the Laser Interferometer Space Antenna (LISA), alternative analysis techniques must be developed. Here we show that models of the noise and signal transfer functions can be used to tease apart the gravitational and instrument noise contributions. We discuss the role of gravitational wave insensitive "null channels" formed from particular combinations of the time delay interferometry, and derive a new combination that maintains this insensitivity for unequal arm length detectors. We show that, in the absence of astrophysical foregrounds, LISA could detect signals with energy densities as low as $\Omega_{\rm gw} = 6 \times 10^{-13}$ with just one month of data. We describe an end-to-end Bayesian analysis pipeline that is able to search for, characterize and assign confidence levels for the detection of a stochastic gravitational wave background, and demonstrate the effectiveness of this approach using simulated data from the third round of Mock LISA Data Challenges.