We present the result of the first deep learning-based search for the
    signature of microlensing in gravitational waves. This search seeks the
    signature induced by lenses with masses between $10^3M_\odot$–$10^5M_\odot$
    from spectrograms of the binary black hole events in the first and second
    gravitational-wave transient catalogs. We use a deep learning model trained
    with spectrograms of simulated noisy gravitational-wave signals to classify the
    events into two classes, lensed or unlensed. We introduce ensemble learning and
    a majority voting-based consistency test for the predictions of ensemble
    learners. The classification scheme of this search primarily classifies one
    event, GW190707_093326, into the lensed class. To verify the primary
    classification of this event, we also examine the median probability to the
    lensed class and observe the resulting value, $0.984^{+0.012}_{-0.342}$, agrees
    with an empirical criterion $>\!0.6$ for claiming the detection of a lensed
    signal. However, the uncertainty of the estimated $p$-value for the median
    probability and error, ranging from 0 to 0.1, convinces us GW190707_093326 is
    less likely a lensed event because it includes $p\!\geq\!0.05$ where the
    unlensed hypothesis is true. Therefore, we conclude our search finds no
    significant evidence of microlensing signature from the evaluated binary black
    hole events.

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