Speaker
Description
The detection of gravitational waves with the Laser Interferometer Gravitational Wave Observatory (LIGO) has provided the tools to probe the furthest reaches of the universe. A rapid follow up to compact binary coalescence (CBC) events and their electromagnetic counterparts is crucial to find short lived transients. After a gravitational wave (GW) detection another particular challenge is determining a fast and efficient way of characterizing events as astrophysical or terrestrial in origin. The mergers themselves provide many data products from low latency CBC search pipelines which can aid in discerning whether or not a GW signal is real. We present an efficient low latency method of alert classification applying Bayes’ Factors into three machine learning classification algorithms: Random Forest (RF), K-Nearest Neighbors (KNN), and Neural network (NN) using event data from the Mock Data Challenge (MDC). We report the true positive rate of the RF, KNN, and NN classifiers as is 0.82, 0.84, and 0.89 respectively.
