Tesi etd-01192022-162955 |
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Tipo di tesi
Tesi di laurea magistrale
Autore
ANDREOLA, PASQUALE
URN
etd-01192022-162955
Titolo
Test of the Lepton Flavor Universality Violation at CMS
Dipartimento
FISICA
Corso di studi
FISICA
Relatori
relatore Dott. Palla, Fabrizio
correlatore Prof. Messineo, Alberto
correlatore Prof. Messineo, Alberto
Parole chiave
- lepton flavor
- lepton flavor violation
Data inizio appello
07/02/2022
Consultabilità
Completa
Riassunto
A test of the Lepton flavor Universality Violation is performed through the measurement
of R(J/ψ) = B(B+c → J/ψτ+ντ)/B(B+c → J/ψμ+νμ). A sample of pp collision data
corresponding to an integrated luminosity of 59.6 fb−1 has been analyzed. It has been
recorded by CMS at LHC at the center of mass energy of 13 TeV. Monte Carlo samples
are re-scaled to the data integrated luminosity with the fit of B+c → J/ψπ+ and B+ →
J/ψK+, used as control channels. These fits are performed through the sum of different
probability density functions of discriminating variables with RooFit. Candidate events
are selected by offline cuts optimized by a genetic algorithm. Some discriminant variables
are presented and evaluated with efficiencies and ROC curves. A binned maximum-
likelihood fit for these discriminating variables is performed using templates of shapes
from Monte Carlo samples. A closure test of the fit procedure is done by splitting the
Monte Carlo sample in half: one is used to extract shapes and the other one to perform
the fit. Finally, this fit procedure is used in the analysis for a preliminary evaluation of
R(J/ψ) and its errors.
of R(J/ψ) = B(B+c → J/ψτ+ντ)/B(B+c → J/ψμ+νμ). A sample of pp collision data
corresponding to an integrated luminosity of 59.6 fb−1 has been analyzed. It has been
recorded by CMS at LHC at the center of mass energy of 13 TeV. Monte Carlo samples
are re-scaled to the data integrated luminosity with the fit of B+c → J/ψπ+ and B+ →
J/ψK+, used as control channels. These fits are performed through the sum of different
probability density functions of discriminating variables with RooFit. Candidate events
are selected by offline cuts optimized by a genetic algorithm. Some discriminant variables
are presented and evaluated with efficiencies and ROC curves. A binned maximum-
likelihood fit for these discriminating variables is performed using templates of shapes
from Monte Carlo samples. A closure test of the fit procedure is done by splitting the
Monte Carlo sample in half: one is used to extract shapes and the other one to perform
the fit. Finally, this fit procedure is used in the analysis for a preliminary evaluation of
R(J/ψ) and its errors.
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