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@thesis{Barbetti:2020msc,
  abbr={M.Sc.},
  title={Techniques for parametric simulation with deep neural networks and implementation for the 
         LHCb experiment at CERN and its future upgrades},
  author={<NAME>},
  abstract={The LHCb experiment is one of the four detector along the accelerator ring of the Large Hadron Collider (LHC) 
            at CERN, and is dedicated to the study of heavy flavour physics in \(pp\) collisions. Its primary goal is to 
            look for indirect evidence of phenomena beyond the Standard Model in \(CP\)-violation and in rare decays of 
            \(b\)- and \(c\)-hadrons. In order to improve its statistical power, starting from the Run 3 of LHC, LHCb 
            will operate with a fully software trigger system, which will provide datasets at least one order of magnitude 
            larger. This will allow to reach unprecedented accuracy as long as the Collaboration will be able provide 
            simulated samples as large. As a direct consequence, the production of such simulated samples will dominate 
            the computing effort of the experiment. Reproducing accurately all the physics processes from the \(pp\) 
            collisions to the radiation-matter interactions within the detectors (the full simulation approach) is already 
            now incapable to sustain the analysis demands of the various physics groups, and it is therefore necessary to 
            adopt faster solutions to take full advantage of the upgraded detector. Ultra-fast simulation needs lower 
            computing resources, renouncing to reproduce radiation-matter interactions and parameterizing directly the 
            high-level response of the detector. The LHCb subsystems are based on various physics processes, also very 
            different, that make building high-level parameterizations non-trivial: for example, the Particle Identification 
            (PID) system combines information from RICH, calorimeter and muon detectors. This task can be carried out 
            effectively by Generative Adversarial Networks (GAN), a powerful class of deep learning algorithms able to 
            reproduce highly faithful and diverse probability distributions thanks to a generative model learned directly 
            from data. A large part of this thesis has concerned the development and implementation of state-of-the-art GAN 
            algorithms to provide the high-level response of the PID subsystems of LHCb. These neural networks were trained 
            over the calibration samples collected in 2016, in order to provide datasets composed by an unbiased selection 
            of long-lived particles. I have modified the learning procedure to subtract statistically the residual background 
            within the training data, and I have developed an independent algorithm capable to measure the quality of the 
            generated samples. This strategy has allowed to build models capable not only to parameterize the high-level 
            response of specific detectors (such as RICH detectors and muon system) to different particles traversing them, 
            but also to reproduce the distributions of variables resulting from the combination of various detector responses. 
            Therefore, given a few basic information such as the particle type, its kinematics and the total number of tracks 
            within the detector, the obtained models are able to synthesize accurately a wide range of probability distributions 
            representing the response obtained from a single detector or from their combination. The second important personal 
            contribution is related to the design and development of the \(\texttt{mambah}\) framework, a Python package aimed 
            to provide and manage user friendly data structures for High Energy Physics applications. All \(\texttt{mambah}\) 
            objects were designed to take full advantage of a batch-grained framework, using the most modern softwares for 
            parallel computing and exploiting efficiently hardware accelerators, such as GPUs or FPGA. Within \(\texttt{mambah}\) 
            project, I have dealt with the implementation of database management functions and to the design of the simulation 
            framework based on \(\texttt{mambah}\), named \(\texttt{mambah.sim}\). The \(\texttt{mambah.sim}\) module allows 
            to selectively generate particles with the kinematics set by the \(pp\) collision and to propagate them within the 
            detector defining custom parameterization functions for efficiencies and resolutions: I have developed the 
            parameterization for the PID system of LHCb. I have proved the correctness of the implemented models, showing the 
            generalization capabilities of GANs in describing decay channels different from the one of training. Lastly, I have 
            shown that the samples produced by \(\texttt{mambah.sim}\) are competitive with the full simulated ones, while 
            ensuring a significant reduction of the computing cost.},
  pdf={master_thesis.pdf},
  slides={slides_master_thesis.pdf},
  school={University of Florence},
  year={2020},
  selected={false}
}

@thesis{Barbetti:2017bsc,
  abbr={B.Sc.},
  title={Study of the charmonium resonances in \(B^{+} \to p \bar p K^{+}\) and 
         \(B^{+} \to p \bar p \gamma K^{+}\) decays with the LHCb experiment at CERN},
  author={<NAME>},
  abstract={Gli stati di quarkonium, formati dalla combinazione quark-antiquark (\(q\bar q\)), sono descritti nel Modello 
            Standard. Lo spettro energetico delineato dalla cromodinamica quantistica (QCD) è caratterizzato da stati che 
            si differenziano per massa, larghezza e numeri quantici, quali momento angolare, parità \(P\) e parità \(C\). 
            Il lavoro di questa tesi si è quindi focalizzato sullo studio del charmonium, cioè il sistema \(c \bar{c}\), 
            prodotto nel decadimento del mesone \(B^+\). I dati analizzati sono stati raccolti nel corso del 2015 e del 
            2016 dall'esperimento LHCb del CERN, uno spettrometro di massa in avanti dedicato alla fisica  dei quark \(b\) 
            e \(c\). Lo studio del canale di decadimento \(B^+ \to (c \bar{c}) K^+\) con \(c \bar{c} \to p \bar{p}\) 
            permette di distinguere vari stati di charmonium. La selezione del decadimento \(B^+ \to p\bar p K^+\) è stata 
            ottimizzata studiando la distribuzione della massa invariante della combinazione \(p\bar p K^+\) a cui è seguita 
            la sottrazione del fondo combinatorio grazie alla tecnica statistica sPlot. L'analisi della distribuzione in 
            massa invariante \(p\bar p\) del campione selezionato mette in evidenza eccessi compatibili con gli stati 
            \(\eta_c\), \(J/\psi\), \(\chi_{c0}\), \(\chi_{c1}\), \(\eta_c(2S)\) e \(\psi(2S)\). I valori di massa e 
            larghezza ottenuti dalla parametrizzazione della distribuzione sono compatibili con le medie mondiali. Ad oggi, 
            la produzione dello stato \(h_c\) nel decadimento di adroni-\(b\) non è mai stata osservata, nonostante ne 
            esistano alcune indicazioni. Nel lavoro di tesi si è affrontata la ricerca del canale \(B^+ \to h_c K^+\) 
            con \(h_c \to \eta_c \gamma\) e \(\eta_c \to p \bar{p}\), affiancato dal canale di controllo  \(B^+ \to \chi_{c1} 
            K^+\) con \(\chi_{c1} \to J/\psi \gamma\) e \(J/\psi \to p \bar{p}\). La parità \(C\) garantisce che le due catene 
            di decadimento rimangano separate, permettendo di distinguere lo stato \(h_c\) da \(\chi_{c1}\) che, con una 
            differenza in massa di pochi \(\rm{MeV}\), risulterebbero altrimenti sovrapposti. La catena di decadimento \(B^+ 
            \to \chi_{c1} K^+\) con \(\chi_{c1} \to J/\psi \gamma\) e \(J/\psi \to p \bar{p}\) è stata messa in evidenza per 
            prima volta ad LHCb, mentre non si è osservato segnale per il decadimento \(B^+ \to h_c K^+\). Si è quindi inoltrata 
            una richiesta ufficiale di produzione Monte Carlo indispensabile per il calcolo delle efficienze di entrambi i 
            canali e per migliorare la strategia di selezione con l'applicazione di tecniche di analisi multivariata, in vista 
            di un'estensione ad un campione più ampio.},
  pdf={bachelor_thesis.pdf},
  slides={slides_bachelor_thesis.pdf},
  school={University of Florence},
  year={2017},
  selected={false}
}
