Publication: DEEP LEARNING APPLIED IN THE CLASSIFICATION OF EVENTS GENERATED AT THE ATLAS EXPERIMENT
dc.contributor.advisor | PEZOA, RAQUEL | |
dc.contributor.author | RODRIGUEZ MORA, JOHN IGNACIO | |
dc.contributor.department | Universidad Técnica Federico Santa María. Departamento de Informática | es_CL |
dc.contributor.other | CARQUIN, EDSON | |
dc.coverage.spatial | Casa Central Valparaíso | es_CL |
dc.date.accessioned | 2021-01-28T16:30:21Z | |
dc.date.available | 2021-01-28T16:30:21Z | |
dc.date.issued | 2021-01 | |
dc.description.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. | es_CL |
dc.description.degree | INGENIERO CIVIL INFORMÁTICO | es_CL |
dc.description.program | DEPARTAMENTO DE INFORMÁTICA. INGENIERÍA CIVIL INFORMÁTICA | es_CL |
dc.format.extent | 84 H. | es_CL |
dc.identifier.barcode | 192559987UTFSM.pdf | es_CL |
dc.identifier.uri | https://hdl.handle.net/11673/49967 | |
dc.subject | CERN | es_CL |
dc.subject | ATLAS | es_CL |
dc.subject | DI-HIGGS | es_CL |
dc.subject | DEEP LEARNING | es_CL |
dc.subject | EVENT CLASSIFICATION | es_CL |
dc.subject | IMBALANCE LEARNING | es_CL |
dc.title | DEEP LEARNING APPLIED IN THE CLASSIFICATION OF EVENTS GENERATED AT THE ATLAS EXPERIMENT | es_CL |
dc.type | Tesis de Pregrado | |
dspace.entity.type | Publication |
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