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

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.

6 pages, 3 figures

References in corpus (10)

• The NumPy array: a structure for efficient numerical computation
• Advanced Virgo: a 2nd generation interferometric gravitational wave detector
• Advanced LIGO
• GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run
• GW190521: A Binary Black Hole Merger with a Total Mass of $150M_{\odot }$
• BayesWave: Bayesian Inference for Gravitational Wave Bursts and Instrument Glitches
• The BayesWave analysis pipeline in the era of gravitational wave observations
• Spectral separation of the stochastic gravitational-wave background for LISA in the context of a modulated Galactic foreground
• Ability of LISA to detect a gravitational-wave background of cosmological origin: the cosmic string case
• Black hole science with the Laser Interferometer Space Antenna

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