scholarly journals Uso de técnicas de Machine Learning en el experimento CMS

2020 ◽  
Vol 1 (3) ◽  
Author(s):  
Cristina Oropeza-Barrera
Keyword(s):  
Machine Learning ◽  
Large Hadron Collider ◽  
Compact Muon Solenoid ◽  
Hadron Collider ◽  
Trabajo Colaborativo

La física experimental de partículas se encuentra en una era dorada llena de retos tecnológicos. Para superarlos, las grandes colaboraciones del LHC (Large Hadron Collider) han implementado técnicas de Machine Learning en sus operaciones con resultados impresionantes. En este documento se resumen algunas de las aplicaciones principales del aprendizaje automatizado, en particular de las redes neuronales artificiales, en el experimento CMS (Compact Muon Solenoid). Además, se resalta la importancia del trabajo colaborativo e interdisciplinario para la correcta implementación e interpretación de estas técnicas de análisis. El objetivo del presente trabajo consiste en despertar el interés por estos temas entre los miembros de las comunidades de física de partículas y ciencias de la computación, con el fin de ampliar las posibilidades de trabajos de investigación conjuntos.

scholarly journals Utilizing Unsupervised Machine Learning in BSM Physics Searches At The LHC

2020 ◽  
Vol 245 ◽  
pp. 06021
Author(s):  
Adam Leinweber ◽  
Martin White
Keyword(s):  
Machine Learning ◽  
Large Hadron Collider ◽  
Hadron Collider ◽  
New Physics ◽  
Machine Learning Technique ◽  
Unsupervised Machine Learning ◽  
Learning Technique ◽  
Bsm Physics ◽  
Supersymmetric Particles ◽  
One Machine

Recent searches for supersymmetric particles at the Large Hadron Collider have been unsuccessful in detecting any BSM physics. This is partially because the exact masses of supersymmetric particles are not known, and as such, searching for them is very difficult. The method broadly used in searching for new physics requires one to optimise on the signal being searched for, potentially suppressing sensitivity to new physics which may actually be present that does not resemble the chosen signal. The problem with this approach is that, in order to detect something with this method, one must already know what to look for. I will showcase one machine-learning technique that can be used to define a “signal-agnostic” search. This is a search that does not make any assumptions about the signal being searched for, allowing it to detect a signal in a more general way. This method is applied to simulated BSM physics data and the results are explored.

Higgs Search with the Compact Muon Solenoid(CMS) detector at the Large Hadron Collider(LHC)

2005 ◽  
Vol 20 (15) ◽  
pp. 3400-3402
Author(s):  
◽  
SATYAKI BHATTACHARYA
Keyword(s):  
Large Hadron Collider ◽  
Standard Model ◽  
Compact Muon Solenoid ◽  
Vector Boson ◽  
Hadron Collider ◽  
Center Of Mass ◽  
Gluon Fusion ◽  
Mass Range ◽  
Early Years ◽  
Higgs Search

The Large Hadron Collider(LHC) is a proton proton collider being built at CERN, Geneva which will collide two 7 TeV proton beams giving a center of mass energy of 14 TeV. The Compact Muon Solenoid (CMS) is a multi-purpose detector at the LHC which is designed to discover the Higgs boson over the mass range of 90 to 1000 GeV. Since LEP searches have put a 95% C.L. lower bound on (standard model) Higgs mass of 114.4 GeV and theory excludes mass above about 1 TeV, CMS should discover the Higgs if it exists. In this paper, we will review CMS's Higgs-discovery potential both in the Standard Model and the Minimal Supersymmetric Standard Model for Higgs bosons produced in gluon-gluon fusion and in vector boson fusion mechanisms. Particular emphasis will be placed on discovery in the early years of running with luminosity of about 2 × 1033cm-2/s.

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