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 ⊙ 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.