Deep Learning Semi-supervised Strategy for Gamma/Hadron Classification of Imaging Atmospheric Cherenkov Telescope Events

Thesis
Deep Learning Semi-supervised Strategy for Gamma/Hadron Classification of Imaging Atmospheric Cherenkov Telescope Events

dc.contributor.department	Departamento de Electrónica
dc.contributor.guia	Araya López, Mauricio Alejandro
dc.contributor.guia	Self-Supervised
dc.coverage.spatial	Campus Casa Central Valparaíso
dc.creator	Riquelme Román, Diego Vicente
dc.date.accessioned	2024-09-25T18:15:17Z
dc.date.available	2024-09-25T18:15:17Z
dc.date.issued	2022-07
dc.description.degree	INGENIERO CIVIL ELECTRÓNICO
dc.description.degree	MAGISTER EN CIENCIAS DE LA INGENIERIA ELECTRONICA
dc.description.program	DEPARTAMENTO DE ELECTRÓNICA. INGENIERÍA CIVIL ELECTRÓNICA
dc.identifier.barcode	191523393UTFSM
dc.identifier.uri	https://repositorio.usm.cl/handle/123456789/8232
dc.identifier.uri	https://doi.org/10.71700/dspace-memorias/1628
dc.rights.accessRights	info:eu-repo/semantics/openAccess
dc.subject	DEEP LEARNING
dc.subject	SELF-SUPERVISED
dc.subject	CHERENKOV
dc.subject	CLASSIFICATION
dc.subject	HIGH-ENERGY ASTRONOMY
dc.subject	CONVOLUTIONAL
dc.title	Deep Learning Semi-supervised Strategy for Gamma/Hadron Classification of Imaging Atmospheric Cherenkov Telescope Events
dc.type	Tesis de Pregrado
dspace.entity.type	Tesis

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