Machine learning techniques for gravitational waves data analysis

The use of machine learning in the study of gravitational wave physics is increasingly widespread. The flexibility and results that this technology has achieved encourage the use and exploration of such techniques in this research field. In this work, we develop a machine learning tool based on the random forest technique to enhance the measurement capabilities of the MBTA (Multi-Band Template Analysis) algorithm in distinguishing signal from noise. The results are obtained by considering different configurations and features, taking into account both physical and statistical values of the triggers to train and test the machine learning algorithm. Comparisons between the statistical significance obtained from machine learning and the classical algorithm were conducted using real data.