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arXiv · 2210.06194

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
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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.

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Charles Badger, Katarina Martinovic, Alejandro Torres-Forné, Mairi Sakellariadou, José A. Font. 2022-10-12. Dictionary learning: a novel approach to detecting binary black holes in the presence of Galactic noise with LISA. https://doi.org/10.1103/physrevlett.130.091401

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