Deep Learning-based Search for Microlensing Signature from Binary Black Hole Events in GWTC-1 and-2

Abstract

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(3) and 10(5) M-circle dot 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 of the lensed class and observe that the resulting value, 0.984(-0.342)(+0.012), 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.