Thesis
DEEP LEARNING APPLIED IN THE CLASSIFICATION OF EVENTS GENERATED AT THE ATLAS EXPERIMENT

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Date
2021-01
Authors
RODRIGUEZ MORA, JOHN IGNACIO
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Abstract
A Toroidal LHC Apparatus (ATLAS) is one of the two general purpose detectors at the Large Hadron Collider (LHC), at Conseil Européen pour la Recherche Nucléaire (CERN). Inside this, bunches of protons collide with a frequency of 40 MHz, and each collision, or event, can produce huge amounts of particles. The classification of LHC events is one of the most important analysis tasks in HEP, and a fundamental work for searching new phenomena. This work is focused in boosted di-Higgs decaying into b ¯bτ +τ −, handled as a classification task using deep learning techniques. Many models were trained, and the best 9 of them are tested, evaluated and compared. The best model resulted from taking an approach with Parameterized Neural Networks (PNN) and Cost-sensitive learning, specifically increasing the background class weight. Scores with these techniques reached above 0.9 F1-score on both background and signal classes. This work is a computer science study in collaboration with the Physics Department and CCTVal.
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Keywords
CERN , ATLAS , DI-HIGGS , DEEP LEARNING , EVENT CLASSIFICATION , IMBALANCE LEARNING
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