Speaker
Description
The search for gravitational wave signals in the data collected by the current ground-based interferometers is a complex problem, especially when only one detector operates. Modern deep learning approaches could contribute to find a solution. I'll discuss the detection problem and present the work detailed in arXiv:2307.09268 where we investigate the performance of neural network classifiers based on three types of architectures: convolutional neural network, temporal convolutional network, and inception time. The last two architectures are specifically designed to process time-series data. We apply the trained classifiers to LIGO data from the O1 science run, focusing specifically on single-detector times. We find a promising candidate on 2016-01-04 12:24:17 UTC compatible with a black hole merger with masses 50 M⊙ and 24 M⊙.
