Contents |
We present the result of the first deep learning-based endeavor for searching the signature of microlensing in gravitational waves. This search seeks the signature induced by lenses with masses between 10^3 M_sun - 10^5 M_sun 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. We observe the median probability of the event, 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 >= 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. |