Studies on RKR_{K} with Large Dilepton Invariant-Mass, Scalable Pythonic Fitting, and Online Event Interpretation with GNNs at LHCb

The Standard Model of particle physics is the established theory describing nature's phenomena involving the most fundamental particles. However, the model has inherent shortcomings, and recent measurements indicate tensions with its predictions, suggesting the existence of a more fundamental theory. Experimental particle physics aims to test the Standard Model predictions with increasing precision in order to constrain or confirm physics beyond the Standard Model. A large part of this thesis is dedicated to the first measurement of the ratio of branching fractions of the decays $B^+ \rightarrow K^+ \mu^+ \mu^-$ and $B^+ \rightarrow K^+ e^+ e^-$, referred to as $R_K$, in the high dilepton invariant mass region. The presented analysis uses the full dataset of proton-proton collisions collected by the LHCb experiment in the years 2011-2018, corresponding to an integrated luminosity of 9~$\mathrm{fb}^{-1}$. The final result for $R_K$ is still blinded. The sensitivity of the developed analysis is estimated to be $\sigma^{(\mathrm{stat})}_{R_K} = 0.073$ and $\sigma^{(\mathrm{syst})}_{R_K} = 0.031$. Applying all analysis steps to a control channel, where the value of $R_K$ is known, successfully recovers the correct value. In addition to the precision measurement of $R_K$ at a high dilepton invariant mass, this thesis contains two more technical topics. First, an algorithm that selects particles in an event in the LHCb detector by performing a full event interpretation, referred to as \textsc{DFEI}. This tool is based on multiple Graph Neural Networks and aims to cope with the increase in luminosity in current and future upgrades of the LHCb detector. Comparisons with the current approach show at least similar, sometimes better, performance with respect to decay reconstruction and selection using charged particles. The efficiency is mostly independent of the luminosity, which is crucial for future upgrades. Second, a \textsc{Python} package for likelihood model fitting called \textsc{zfit}. The increasing popularity of the \textsc{Python} programming language in High Energy Physics creates a need for a flexible, modular, and performant fitting library. The \textsc{zfit} package is well integrated into the \textsc{Python} ecosystem, highly customizable and fast thanks to its computational backend \textsc{TensorFlow}

Studies on RKR_K with Large Dilepton Invariant-Mass, Scalable Pythonic Fitting, and Online Event Interpretation with GNNs at LHCb

The Standard Model of particle physics is well established, yet recently showed tensions with experimental observations. A large part of this thesis is dedicated to the first measurement of the ratio of branching fractions of the decays $B^+ \rightarrow K^+ \mu^+ \mu^-$ and $B^+ \rightarrow K^+ e^+ e^-$ , referred to as $R_K$ , in the high dilepton invariant-mass region. The presented analysis uses the full dataset of proton-proton collisions collected by the LHCb experiment in the years 2011-2018, corresponding to an integrated luminosity of 9 $fb^{-1}$. The final result for $R_K$ is still blinded. The sensitivity of the developed analysis is estimated to be $\sigma_{R_K}^{\mathrm{stat}} = 0.073$ and $\sigma_{R_K}^{\mathrm{stat}} = 0.031$. In addition to the precision measurement of $R_K$ at a high dilepton invariant mass, this thesis contains two more technical topics. First, an algorithm that selects particles in an event in the LHCb detector by performing a full event interpretation, referred to as DFEI. This tool is based on multiple Graph Neural Networks and aims to cope with the increase in luminosity in current and future upgrades of the LHCb detector. Comparisons with the current approach show at least similar, sometimes better, performance with respect to decay reconstruction and selection using charged particles. The efficiency is mostly independent of the luminosity, which is crucial for future upgrades. Second, a Python package for likelihood model fitting called zfit. The increasing popularity of the Python programming language in High Energy Physics creates a need for a flexible, modular, and performant fitting library. The zfit package is well integrated into the Python ecosystem, highly customizable and fast thanks to its computational backend TensorFlow.Comment: PhD thesis, 269 pages, 130 figures, contains parts of arxiv:1910.13429 and arxiv:2304.08610, future publication on RK comin

zfit: scalable pythonic fitting

Statistical modeling is a key element in many scientific fields and especially in High-Energy Physics (HEP) analysis. The standard framework to perform this task in HEP is the C++ ROOT/RooFit toolkit; with Python bindings that are only loosely integrated into the scientific Python ecosystem. In this paper, zfit, a new alternative to RooFit written in pure Python, is presented. Most of all, zfit provides a well defined high-level API and workflow for advanced model building and fitting, together with an implementation on top of TensorFlow, allowing a transparent usage of CPUs and GPUs. It is designed to be extendable in a very simple fashion, allowing the usage of cutting-edge developments from the scientific Python ecosystem in a transparent way. The main features of zfit are introduced, and its extension to data analysis, especially in the context of HEP experiments, is discussed.Comment: 12 pages, 2 figure

GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions

The LHCb experiment at the Large Hadron Collider (LHC) is designed to perform high-precision measurements of heavy-hadron decays, which requires the collection of large data samples and a good understanding and suppression of multiple background sources. Both factors are challenged by a five-fold increase in the average number of proton-proton collisions per bunch crossing, corresponding to a change in the detector operation conditions for the LHCb Upgrade I phase, recently started. A further ten-fold increase is expected in the Upgrade II phase, planed for the next decade. The limits in the storage capacity of the trigger will bring an inverse relation between the amount of particles selected to be stored per event and the number of events that can be recorded, and the background levels will raise due to the enlarged combinatorics. To tackle both challenges, we propose a novel approach, never attempted before in a hadronic collider: a Deep-learning based Full Event Interpretation (DFEI), to perform the simultaneous identification, isolation and hierarchical reconstruction of all the heavy-hadron decay chains per event. This approach radically contrasts with the standard selection procedure used in LHCb to identify heavy-hadron decays, that looks individually at sub-sets of particles compatible with being products of specific decay types, disregarding the contextual information from the rest of the event. We present the first prototype for the DFEI algorithm, that leverages the power of Graph Neural Networks (GNN). This paper describes the design and development of the algorithm, and its performance in Upgrade I simulated conditions

Multidifferential study of identified charged hadron distributions in ZZ-tagged jets in proton-proton collisions at s=\sqrt{s}=13 TeV

Jet fragmentation functions are measured for the first time in proton-proton collisions for charged pions, kaons, and protons within jets recoiling against a $Z$ boson. The charged-hadron distributions are studied longitudinally and transversely to the jet direction for jets with transverse momentum 20 $< p_{\textrm{T}} < 100$ GeV and in the pseudorapidity range $2.5 < \eta < 4$. The data sample was collected with the LHCb experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 1.64 fb$^{-1}$. Triple differential distributions as a function of the hadron longitudinal momentum fraction, hadron transverse momentum, and jet transverse momentum are also measured for the first time. This helps constrain transverse-momentum-dependent fragmentation functions. Differences in the shapes and magnitudes of the measured distributions for the different hadron species provide insights into the hadronization process for jets predominantly initiated by light quarks.Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-013.html (LHCb public pages

Study of the B−→Λc+Λˉc−K−B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-} decay

The decay $B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-}$ is studied in proton-proton collisions at a center-of-mass energy of $\sqrt{s}=13$ TeV using data corresponding to an integrated luminosity of 5 $\mathrm{fb}^{-1}$ collected by the LHCb experiment. In the $\Lambda_{c}^+ K^{-}$ system, the $\Xi_{c}(2930)^{0}$ state observed at the BaBar and Belle experiments is resolved into two narrower states, $\Xi_{c}(2923)^{0}$ and $\Xi_{c}(2939)^{0}$, whose masses and widths are measured to be $$m(\Xi_{c}(2923)^{0}) = 2924.5 \pm 0.4 \pm 1.1 \,\mathrm{MeV}, \\ m(\Xi_{c}(2939)^{0}) = 2938.5 \pm 0.9 \pm 2.3 \,\mathrm{MeV}, \\ \Gamma(\Xi_{c}(2923)^{0}) = \phantom{000}4.8 \pm 0.9 \pm 1.5 \,\mathrm{MeV},\\ \Gamma(\Xi_{c}(2939)^{0}) = \phantom{00}11.0 \pm 1.9 \pm 7.5 \,\mathrm{MeV},$$ where the first uncertainties are statistical and the second systematic. The results are consistent with a previous LHCb measurement using a prompt $\Lambda_{c}^{+} K^{-}$ sample. Evidence of a new $\Xi_{c}(2880)^{0}$ state is found with a local significance of $3.8\,\sigma$, whose mass and width are measured to be $2881.8 \pm 3.1 \pm 8.5\,\mathrm{MeV}$ and $12.4 \pm 5.3 \pm 5.8 \,\mathrm{MeV}$, respectively. In addition, evidence of a new decay mode $\Xi_{c}(2790)^{0} \to \Lambda_{c}^{+} K^{-}$ is found with a significance of $3.7\,\sigma$. The relative branching fraction of $B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-}$ with respect to the $B^{-} \to D^{+} D^{-} K^{-}$ decay is measured to be $2.36 \pm 0.11 \pm 0.22 \pm 0.25$, where the first uncertainty is statistical, the second systematic and the third originates from the branching fractions of charm hadron decays.Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-028.html (LHCb public pages

Measurement of the ratios of branching fractions R(D∗)\mathcal{R}(D^{*}) and R(D0)\mathcal{R}(D^{0})

The ratios of branching fractions $\mathcal{R}(D^{*})\equiv\mathcal{B}(\bar{B}\to D^{*}\tau^{-}\bar{\nu}_{\tau})/\mathcal{B}(\bar{B}\to D^{*}\mu^{-}\bar{\nu}_{\mu})$ and $\mathcal{R}(D^{0})\equiv\mathcal{B}(B^{-}\to D^{0}\tau^{-}\bar{\nu}_{\tau})/\mathcal{B}(B^{-}\to D^{0}\mu^{-}\bar{\nu}_{\mu})$ are measured, assuming isospin symmetry, using a sample of proton-proton collision data corresponding to 3.0 fb${ }^{-1}$ of integrated luminosity recorded by the LHCb experiment during 2011 and 2012. The tau lepton is identified in the decay mode $\tau^{-}\to\mu^{-}\nu_{\tau}\bar{\nu}_{\mu}$. The measured values are $\mathcal{R}(D^{*})=0.281\pm0.018\pm0.024$ and $\mathcal{R}(D^{0})=0.441\pm0.060\pm0.066$, where the first uncertainty is statistical and the second is systematic. The correlation between these measurements is $\rho=-0.43$. Results are consistent with the current average of these quantities and are at a combined 1.9 standard deviations from the predictions based on lepton flavor universality in the Standard Model.Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-039.html (LHCb public pages

raredecay: MVA and reweighting with Machine Learning

Machine Learning based Analysis Framework for physics on top of RE