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

Kyungmin Kim; Joongoo Lee; Otto A. Hannuksela; Tjonnie G. F. Li

doi:10.3847/1538-4357/ac92f3

paper

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

arXiv:2206.08234 · doi:10.3847/1538-4357/ac92f3

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 $1 0^{3} M_{⊙}$ -- $1 0^{5} M_{⊙}$ 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 to the lensed class and observe the resulting value, $0.98 4_{- 0.342}^{+ 0.012}$ , 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 $p \geq 0.05$ 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.

arXiv comment14 pages, 7 figures, 4 tables, Accepted by Astrophys. J

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

Abstract

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