CMS level-1 trigger muon momentum assignment with machine learning

Diotalevi, Tommaso (2018) CMS level-1 trigger muon momentum assignment with machine learning. [Laurea magistrale], Università di Bologna, Corso di Studio in Fisica [LM-DM270]
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Abstract

With the advent of the High-Luminosity phase of the LHC (HL-LHC), the instantaneous luminosity of the Large Hadron Collider at CERN will increase up to 7,5 10^34 cm^-2s^-1. Therefore, new algorithmic techniques for data acquisition and processing will be necessary, in preparation for a high pile-up environment that would eventually make the current electronics and trigger devices obsolete. Nowadays, Machine Learning techniques represent a promising alternative to this problem, as they make possible the selection of multiple information - collected by the detector - and build from them different models, able to predict with a certain efficiency fundamental physical quantities, including the transverse momentum pT. The analysis presented in this Master Thesis consists in the production of such models - with data obtained through Monte Carlo simulations - capable of predicting the transverse momentum of muons crossing the Barrel region of the CMS muon chambers, and compare the results with the pT assigned by the current CMS Level 1 Barrel Muon Track Finder (BMTF) trigger system.

Abstract
Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Diotalevi, Tommaso
Relatore della tesi
Correlatore della tesi
Scuola
Corso di studio
Indirizzo
Curriculum B: Fisica nucleare e subnucleare
Ordinamento Cds
DM270
Parole chiave
physics,machine learning,lhc,trigger,muon,CMS
Data di discussione della Tesi
20 Luglio 2018
URI

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