The CMS collaboration at CERN aims to study the phenomenon of CP symmetry violation by measuring the time-dependent flavour oscillation of neutral B mesons. For this purpose, it is necessary to consider, among others, the semileptonic decays of the type B→ D μ ν. In order to determine the proper decay time in such events, it is crucial to be able to reconstruct the energy of the decayed B meson with sufficient precision and accuracy, despite the fact that the neutrino involved cannot be detected. This is the aim of this thesis work, in which the analysis was conducted on a sample of Monte Carlo simulated events generated using the packages Pythia and EvtGen. After introducing the kinematic variable of the corrected invariant mass, which is useful for the energy reconstruction of the decay, a neural network regression was implemented to reconstruct the B meson energy from only the experimentally known variables identified as most effective. Using this method, it was possible to obtain an average bias from the true value of 0.7%, with a standard deviation of 11.8%, on 45.8% of the available events.
La collaborazione CMS del CERN intende studiare il fenomeno della violazione di simmetria CP mediante misure dell’oscillazione di sapore dei mesoni B neutri in funzione del tempo. A tal fine è necessario considerare, tra gli altri, anche i decadimenti semileptonici del tipo B→ D μ ν. Per misurare il tempo proprio di decadimento in tali eventi, è fondamentale poter ricostruire con sufficiente precisione e accuratezza l’energia del mesone B decaduto, nonostante non sia possibile rivelare il neutrino coinvolto. Ciò rappresenta l’obiettivo del presente lavoro di tesi, in cui l’analisi è stata condotta su un campione di eventi simulati Monte Carlo, generati con i pacchetti Pythia ed EvtGen. Dopo aver introdotto la variabile cinematica massa invariante corretta, utile ai fini della ricostruzione energetica del decadimento, si è implementata una regressione con rete neurale che ricostruisce l’energia del B a partire dalle sole variabili sperimentalmente note che sono state identificate come più efficaci. Con tale metodo è stato possibile ottenere una distorsione media dal valor vero dello 0.7%, con deviazione standard dell’11.8%, sul 45.8% degli eventi a disposizione.
Studio del decadimento semileptonico del mesone B a CMS
CAVALLIN, JONATHAN
2023/2024
Abstract
The CMS collaboration at CERN aims to study the phenomenon of CP symmetry violation by measuring the time-dependent flavour oscillation of neutral B mesons. For this purpose, it is necessary to consider, among others, the semileptonic decays of the type B→ D μ ν. In order to determine the proper decay time in such events, it is crucial to be able to reconstruct the energy of the decayed B meson with sufficient precision and accuracy, despite the fact that the neutrino involved cannot be detected. This is the aim of this thesis work, in which the analysis was conducted on a sample of Monte Carlo simulated events generated using the packages Pythia and EvtGen. After introducing the kinematic variable of the corrected invariant mass, which is useful for the energy reconstruction of the decay, a neural network regression was implemented to reconstruct the B meson energy from only the experimentally known variables identified as most effective. Using this method, it was possible to obtain an average bias from the true value of 0.7%, with a standard deviation of 11.8%, on 45.8% of the available events.File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.12608/68322