Uso de técnicas de Machine Learning en el experimento CMS

Cristina Oropeza-Barrera

doi:10.22201/fesa.figuras.2020.1.3.121

Authors

Cristina Oropeza-Barrera Universidad Iberoamericana, Ciudad de México (MX) https://orcid.org/0000-0001-9724-0016

DOI:

https://doi.org/10.22201/fesa.figuras.2020.1.3.121

Keywords:

Machine Learning, Experimental Particle Physics, Large Hadron Collider, Compact Muon Solenoid experiment, Automated Machine Learning

Abstract

Experimental Particle Physics is in a golden era full of technological challenges. To overcome them, the substantial collaborations within the LHC (Large Hadron Collider) have implemented Machine Learning techniques in its operations with impressive results. This document summarizes some of the main applications of Automated Machine Learning, particularly of Artificial Neural Networks, in the CMS (Compact Muon Solenoid) experiment. In addition, the importance of collaborative and interdisciplinary work for the correct implementation and interpretation of these analysis techniques is highlighted. The aim of this work is to awaken interest in these topics among the members of Particle Physics and Computer Science communities, in order to enhance the possibilities of joint research projects.

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Author Biography

Cristina Oropeza-Barrera, Universidad Iberoamericana, Ciudad de México

Teacher and researcher at the Universidad Iberoamericana, Ciudad de México. Graduated in Physics Engineering from the same institution, she started her PhD studies in Experimental Particles Physics at Glasgow University, United Kingdom. Her research on Elemental Particles is conducted within the CMS experiment of the Large Hadron Collider (LHC) in Switzerland, where she also carries out science and engineering diffusion activities, particularly among women. She has been coordinator of Physics and Engineering Physics in the Physics and Mathematics Department at the Universidad Iberoamericana Ciudad de México. She currently serves as scholar and tutor for the Transitional Engineering program at the Universidad Iberoamericana Tijuana.

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