Data Analysis of Particle Physics Experiments Based on Machine Learning and the Mitchell’s Criteria

• Autor: Nieto-Chaupis, Huber
• Formato: artículo
• Fecha de Publicación: 2020
• Institución: Universidad Autónoma del Perú
• Repositorio: AUTONOMA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.autonoma.edu.pe:20.500.13067/1714
• Enlace del recurso: https://hdl.handle.net/20.500.13067/1714; https://doi.org/10.1007/978-3-030-46785-2_29
• Nivel de acceso: acceso restringido
• Materia: Data analysis; Particle Physics Experiments; Machine learning; https://purl.org/pe-repo/ocde/ford#2.02.04

Commonly the searching and identification of new particles, requires to reach highest efficiencies and purities as well. It demands to apply a chain of cuts that reject the background substantially. In most cases the processes to extract signal from the background is carried out by hand with some assistance of well designed and intelligent codes that save time and resources in high energy physics experiments. In this paper we present one application of the Mitchell’s criteria to extract efficiently beyond Standard Model signal events yielding an error of order of 1.22%. The usage of Machine Learning schemes appears to be advantageous when large volumes of data need to be scrutinized.