Dictionary learning: a novel approach to detecting binary black holes in the presence of Galactic noise with LISA

TL;DR

This paper introduces a dictionary learning method to effectively reconstruct merger waveforms of binary black holes in LISA data, overcoming Galactic noise challenges and enabling detection at high redshifts.

Contribution

The study presents a novel dictionary learning approach for detecting binary black hole mergers in noisy LISA data, demonstrating high success rates for certain mass ranges and redshifts.

Findings

Effective waveform reconstruction for binaries >~3000 M_sun

Successful detection up to redshift 7.5 in optimistic scenarios

Reliable results when SNR is approximately 5 or higher

Abstract

The noise produced by the inspiral of millions of white dwarf binaries in the Milky Way may pose a threat to one of the main goals of the space-based LISA mission: the detection of massive black hole binary mergers. We present a novel study for reconstruction of merger waveforms in the presence of Galactic confusion noise using dictionary learning. We discuss the limitations of untangling signals from binaries with total mass from $10^2 M_{\odot}$ to $10^4 M_{\odot}$. Our method proves extremely successful for binaries with total mass greater than $\sim 3\times 10^3$ $ M_{\odot}$ up to redshift 3 in conservative scenarios, and up to redshift 7.5 in optimistic scenarios. In addition, consistently good waveform reconstruction of merger events is found if the signal-to-noise ratio is approximately 5 or greater.