White dwarf binary modulation can help stochastic gravitational wave background search

TL;DR

This paper introduces a novel, model-independent filtering technique leveraging the annual modulation of white dwarf binary signals to improve the detection of stochastic gravitational wave backgrounds in space-based interferometers like LISA.

Contribution

The paper proposes a new filtering method based on annual modulation, enhancing SGWB detection without requiring additional information beyond gravitational wave data.

Findings

Improves detection sensitivity for weaker SGWB signals.

Enhances Bayesian evidence for stronger signals.

Demonstrates effectiveness with LISA data challenge simulations.

Abstract

For the stochastic gravitational wave backgrounds (SGWBs) search centred at the milli-Hz band, the galactic foreground produced by white dwarf binaries (WDBs) within the Milky Way contaminates the extra-galactic signal severely. Because of the anisotropic distribution pattern of the WDBs and the motion of the spaceborne gravitational wave interferometer constellation, the time-domain data stream will show an annual modulation. This property is fundamentally different from those of the SGWBs. In this Letter, we propose a new filtering method for the data vector based on the annual modulation phenomenon. We apply the resulted inverse variance filter to the LISA data challenge. The result shows that for the weaker SGWB signal, such as energy density $\Omega_{\rm astro}=1\times10^{-12}$, the filtering method can enhance the posterior distribution peak prominently. For the stronger signal, such as $\Omega_{\rm astro}=3\times10^{-12}$, the method can improve the Bayesian evidence from `substantial' to `strong' against null hypotheses. This method is model-independent and self-contained. It does not ask for other types of information besides the gravitational wave data.