Comparison of different ML methods applied to the classification of events with ttbar in the final state at the ATLAS experiment

This contribution describes the experience with the application of different Machine Learning (ML) techniques to a physics analysis case. The use case chosen is the classification of top-antitop events coming from BSM or from SM using data from a repository of simulated events. The features of these...

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Bibliographic Details
Main Authors:	Martínez, Samuel Campo, Salt, José, de la Hoz, Santiago González, Villaplana, Miguel
Format:	Journal Article
Language:	English
Published:	14-05-2020
Subjects:	Physics - Data Analysis, Statistics and Probability Physics - High Energy Physics - Experiment
Online Access:	Get full text
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Summary:	This contribution describes the experience with the application of different Machine Learning (ML) techniques to a physics analysis case. The use case chosen is the classification of top-antitop events coming from BSM or from SM using data from a repository of simulated events. The features of these events are represented by their kinematic observables. The initial objective was to compare different ML methods in order to see whether they can lead to an improvement in the classification, but the work has also helped us to test many variations in the methods by changing hyper-parameters, using different optimisers, ensembles, etc. With this information we have been able to conduct a comparative study that is useful for ensuring as complete control as possible of the methodology.
Bibliography:	PROC-CTD19-012
DOI:	10.48550/arxiv.2006.02135