Orateur
Description
When searching for compact binary mergers using matched filtering, we usually generate candidate events by finding the local maxima of the signal-to-noise ratio as a function of the search parameters, applying a threshold to them, and then forming a ranking statistic that combines the signal-to-noise ratio with signal-based discriminators, to account for nonstationary and non-Gaussian noise. I will present a proof-of-concept study that uses matched filtering in a different way, namely feeding the ensemble of its results into a convolutional neural network. This leads directly to a ranking statistic that accounts for the collective behavior of the signal-to-noise ratio over a given window of the search space. Using simulated noise and signals, I will show the behavior of this ranking statistic and compare it to conventional quantities like the maximum signal-to-noise ratio and the chi^2-weighted maximum signal-to-noise ratio.