[Submitted on 2 Nov 2022]

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    Abstract: Traditionally, gravitational waves are detected with techniques such as
    matched filtering or unmodeled searches based on wavelets. However, in the case
    of generic black hole binaries with non-aligned spins, if one wants to explore
    the whole parameter space, matched filtering can become impractical, which sets
    severe restrictions on the sensitivity and computational efficiency of
    gravitational-wave searches. Here, we use a novel combination of
    machine-learning algorithms and arrive at sensitive distances that surpass
    traditional techniques in a specific setting. Moreover, the computational cost
    is only a small fraction of the computational cost of matched filtering. The
    main ingredients are a 54-layer deep residual network (ResNet), a Deep Adaptive
    Input Normalization (DAIN), a dynamic dataset augmentation, and curriculum
    learning, based on an empirical relation for the signal-to-noise ratio. We
    compare the algorithm’s sensitivity with two traditional algorithms on a
    dataset consisting of a large number of injected waveforms of non-aligned
    binary black hole mergers in real LIGO O3a noise samples. Our machine-learning
    algorithm can be used in upcoming rapid online searches of gravitational-wave
    events in a sizeable portion of the astrophysically interesting parameter
    space. We make our code, AResGW, and detailed results publicly available at
    this https URL.

    Submission history

    From: Nikolaos Stergioulas [view email]

    Wed, 2 Nov 2022 23:45:50 UTC (1,669 KB)

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