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 Mexico 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.

Downloads

Download data is not yet available.

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.

References

ALICE Collaboration, “The ALICE experiment at the CERN LHC,” JINST 3, (2008): S08002.

ATLAS Collaboration, “Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC,” Phys. Lett. B 716, (2012): 1.

ATLAS Collaboration, “The ATLAS experiment at the CERN Large Hadron Collider,” JINST 3, (2008): S08003.

Calabria, C., “Monitoring tools for the CMS muon detector: present workows and future automation,” EPJ Web of Conferences (CHEP 2018) 214, (2019): 06001.

Charpak, G. et al., “The use of multiwire proportional counters to select and localize charged particles,” Nucl. Instr. Methods 62, (1968): 262.

CMS Collaboration, “Heavy avor identication at CMS with deep neural networks,” CMS-DP-2017-005, (2017).

CMS Collaboration, “Machine learning-based identication of highly Lorentz-boosted hadronically decaying particles at the CMS experiment,” CMS PAS JME-18-002, (2019).

CMS Collaboration, “Measurements of Higgs boson properties in the diphoton decay channel in proton-proton collisions at ps = 13 TeV,” CMS-HIG-16-040, (2018).

CMS Collaboration, “Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC,” Phys. Lett. B 716, (2012): 30.

CMS Collaboration, “The CMS experiment at the CERN LHC,” JINST 3, 2008: S08004.

Deja, K., “Using machine learning techniques for Data Quality Monitoring in CMS and ALICE,” Proceedings of Science (LHCP2019): 236.

Di Florio, A. et al., “Convolutional Neural Network for Track Seed Filtering at the CMS HighLevel Trigger,” J. Phys.: Conf. Ser. 1085, (2018): 042040.

Englert, F. and R. Brout, “Broken symmetry and the mass of gauge vector mesons,” Phys. Rev. Lett. 13, (1964): 321.

Farrell, S. et al., “The HEP.TrkX Project: deep neural networks for HL-LHC online and o_ine tracking,” EPJ Web Conf. 150 (2017): 00003.

Higgs, P. W., “Broken symmetries and the masses of gauge bosons,” Phys. Rev. Lett. 13, (1964): 508.

Higgs, P. W., “Broken symmetries, massless particles and gauge _elds,” Phys. Lett. 12, (1964): 132.

IML, “Who we are. Ginebra, Suiza,” IML: Inter-Experimental LHC Machine Learning Working Group (2018). https://iml.web.cern.ch/ Revisado el 20 de marzo, 2020.

Kaggle Inc., “Higgs challenge,” Kaggle (2018). https://www.kaggle.com/c/higgs-boson Revisado el 20 de marzo, 2020.

LCG Collaboration, “LHC Computing Grid-Technical Design Report,” LCG-TDR-001 (2005).

LHCb Collaboration, “The LHCb detector at the LHC,” JINST 3, (2008): S08005.

SAS Institute Inc., “Machine Learning. What it is and why it matters,” SAS- THE POWER TO KNOW. (2018) https://www.sas.com/...ytics/machine-learning.html Revisado el 20 de marzo, 2020.