Dictionary Learning: A Novel Approach to Detecting Binary Black Holes in the Presence of Galactic Noise with LISA

Charles Badger, Katarina Martinovic, Alejandro Torres-Forné, Mairi Sakellariadou, José A. Font

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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

to
10
4


M

. Our method proves extremely successful for binaries with total mass greater than

3
×
10
3


M

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.
Original languageEnglish
Article number091401
JournalPhysical Review Letters
Volume130
Issue number9
DOIs
Publication statusPublished - 3 Mar 2023

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