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 $10^3{M}_{\odot }$--$10^5{M}_{\odot }$ 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, $0.984_{-0.342}^{+0.012}$, 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 $p\ge 0.05$ 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.

14 pages, 7 figures, 4 tables, Accepted by Astrophys. J

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• The First Model-Independent Chromatic Microlensing Search: No Evidence in the Gravitational Wave Catalog of LIGO-Virgo-KAGRA
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