Convolutional Neural Networks (CNNs) have demonstrated potential for the
    real-time analysis of data from gravitational-wave detector networks for the
    specific case of signals from coalescing compact-object binaries such as
    black-hole binaries. Unfortunately, training these CNNs requires a precise
    model of the target signal; they are therefore not applicable to a wide class
    of potential gravitational-wave sources, such as core-collapse supernovae and
    long gamma-ray bursts, where unknown physics or computational limitations
    prevent the development of comprehensive signal models. We demonstrate for the
    first time a CNN with the ability to detect generic signals — those without a
    precise model — with sensitivity across a wide parameter space. Our CNN has a
    novel structure that uses not only the network strain data but also the Pearson
    cross-correlation between detectors to distinguish correlated
    gravitational-wave signals from uncorrelated noise transients. We demonstrate
    the efficacy of our CNN using data from the second LIGO-Virgo observing run,
    and show that it has sensitivity comparable to that of the “gold-standard”
    transient searches currently used by LIGO-Virgo, at extremely low (order of 1
    second) latency and using only a fraction of the computing power required by
    existing searches, allowing our models the possibility of true real-time
    detection of gravitational-wave transients associated with gamma-ray bursts,
    core-collapse supernovae, and other relativistic astrophysical phenomena.

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