Speaker
Evdokia Chrysovalanti Koursoumpa
(Aristotle University of Thessaloniki)
Description
We used AresGW, a deep residual neural network for gravitational wave detection, to process O3 data from the two LIGO detectors, generating a list of triggers with high significance. To improve the false alarm rate, we also used Gravity Spy, a convolutional neural network designed to identify glitches. We processed ARESGW triggers by removing those that were common between the two networks. Filtering out these common triggers, together with other filtering techniques employed in AresGW, was important to reduce the false alarm rate of several new candidate events, the first to be identified by a machine-learning pipeline.
Primary author
Evdokia Chrysovalanti Koursoumpa
(Aristotle University of Thessaloniki)
Co-author
Prof.
Nikolaos Stergioulas
(Aristotle University of Thessaloniki)
