Speaker
Description
Binary black hole mergers can be located by collecting and analyzing the unique gravitational wave signals they produce. Deep learning computational models, specifically Aframe, are used to identify and filter gravitational wave signals more accurately and in less time than traditional matched filtering analyses. The current machine learning model that we use, Aframe, was originally developed with only the LIGO detectors in mind. However, VIRGO detectors provide additional data from gravitational wave signals that could potentially enhance the detection of binary black hole mergers. We developed a model with Aframe incorporating both LIGO and VIRGO data. Our results showed that the addition of VIRGO data increased the detection of higher mass mergers, however, there was a decrease in the detection of lower mass mergers. This drop in performance for lower masses could be due to VIRGO running on a vastly different sensitivity than LIGO detectors which is something we are continuing to investigate.
