Lamarr: LHCb ultra-fast simulation based on machine learning models deployed within Gauss

About 90% of the computing resources available to the LHCb experiment has been spent to produce simulated data samples for Run 2 of the Large Hadron Collider at CERN. The upgraded LHCb detector will be able to collect larger data samples, requiring many more simulated events to analyze the data to be collected in Run 3. Simulation is a key necessity of analysis to interpret signal, reject background and measure efficiencies. The needed simulation will far exceed the pledged resources, requiring an evolution in technologies and techniques to produce these simulated data samples. In this contribution, we discuss Lamarr, a Gaudi-based framework to speed-up the simulation production parameterizing both the detector response and the reconstruction algorithms of the LHCb experiment. Deep Generative Models powered by several algorithms and strategies are employed to effectively parameterize the high-level response of the single components of the LHCb detector, encoding within neural networks the experimental errors and uncertainties introduced in the detection and reconstruction phases. Where possible, models are trained directly on real data, statistically subtracting any background components by applying appropriate reweighing procedures. Embedding Lamarr in the general LHCb Gauss Simulation framework allows to combine its execution with any of the available generators in a seamless way. The resulting software package enables a simulation process independent of the detailed simulation used to date.Comment: Under review in Journal of Physics: Conference Series (ACAT 2022

Towards Reliable Neural Generative Modeling of Detectors

The increasing luminosities of future data taking at Large Hadron Collider and next generation collider experiments require an unprecedented amount of simulated events to be produced. Such large scale productions demand a significant amount of valuable computing resources. This brings a demand to use new approaches to event generation and simulation of detector responses. In this paper, we discuss the application of generative adversarial networks (GANs) to the simulation of the LHCb experiment events. We emphasize main pitfalls in the application of GANs and study the systematic effects in detail. The presented results are based on the Geant4 simulation of the LHCb Cherenkov detector.Comment: 6 pages, 4 figure

The LHCb ultra-fast simulation option, Lamarr: design and validation

Detailed detector simulation is the major consumer of CPU resources at LHCb, having used more than 90% of the total computing budget during Run 2 of the Large Hadron Collider at CERN. As data is collected by the upgraded LHCb detector during Run 3 of the LHC, larger requests for simulated data samples are necessary, and will far exceed the pledged resources of the experiment, even with existing fast simulation options. An evolution of technologies and techniques to produce simulated samples is mandatory to meet the upcoming needs of analysis to interpret signal versus background and measure efficiencies. In this context, we propose Lamarr, a Gaudi-based framework designed to offer the fastest solution for the simulation of the LHCb detector. Lamarr consists of a pipeline of modules parameterizing both the detector response and the reconstruction algorithms of the LHCb experiment. Most of the parameterizations are made of Deep Generative Models and Gradient Boosted Decision Trees trained on simulated samples or alternatively, where possible, on real data. Embedding Lamarr in the general LHCb Gauss Simulation framework allows combining its execution with any of the available generators in a seamless way. Lamarr has been validated by comparing key reconstructed quantities with Detailed Simulation. Good agreement of the simulated distributions is obtained with two-order-of-magnitude speed-up of the simulation phase.Comment: Under review in EPJ Web of Conferences (CHEP 2023

The LHCb ultra-fast simulation option, Lamarr design and validation

Detailed detector simulation is the major consumer of CPU resources at LHCb, having used more than 90% of the total computing budget during Run 2 of the Large Hadron Collider at CERN. As data is collected by the upgraded LHCb detector during Run 3 of the LHC, larger requests for simulated data samples are necessary, and will far exceed the pledged resources of the experiment, even with existing fast simulation options. The evolution of technologies and techniques for simulation production is then mandatory to meet the upcoming needs for the analysis of most of the data collected by the LHCb experiment. In this context, we propose Lamarr, a Gaudi-based framework designed to offer the fastest solution for the simulation of the LHCb detector. Lamarr consists of a pipeline of modules parameterizing both the detector response and the reconstruction algorithms of the LHCb experiment. Most of the parameterizations are made of Deep Generative Models and Gradient Boosted Decision Trees trained on simulated samples or alternatively, where possible, on real data. Embedding Lamarr in the general LHCb Gauss Simulation framework allows combining its execution with any of the available generators in a seamless way. Lamarr has been validated by comparing key reconstructed quantities with Detailed Simulation. Good agreement of the simulated distributions is obtained with two order of magnitude speed-up of the simulation phase

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

Studio delle risonanze di stati di charmonium nei decadimenti B+→ppˉK+B^+ \to p \bar{p} K^+ e B+→ppˉγK+B^+ \to p \bar{p} \gamma K^+ con l'esperimento LHCb al CERN

Il lavoro di tesi svolto è un approfondimento di fisica adronica incentrato sullo studio degli stati di charmonium e delle relative tecniche sperimentali di rivelazione e analisi. Grazie ad un finanziamento bandito dall’Università di Firenze, ottenuto tramite concorso pubblico, ho trascorso parte del periodo di tesi presso l’esperimento LHCb al CERN occupandomi, oltre che dell’analisi dati, della caratterizzazione di sistemi di reti rilevanti per l’upgrade di LHCb che sarà installato a partire dal 2019. L’attività di analisi dati è stata favorita dalle risorse informatiche offerte dal CERN e si è conclusa con una presentazione al physics working group dedicato allo studio degli stati di quarkonium. In quel contesto, ho richiesto ed ottenuto l’approvazione per la produzione di campioni di dati simulati necessari a completare l’analisi. Nel corso del lavoro di tesi, ho avuto l’opportunità di familiarizzare con pacchetti software ampiamente utilizzati in ambito scientifico, quali git, ROOT e Python; ho trascorso alcune giornate nella sala di controllo dell’esperimento durante la presa-dati, oltre ad aver presentato e discusso lo stato del mio lavoro con esperti italiani e stranieri. La presente tesi è incentrata sull’attività di analisi dei canali di decadimento $B^{+} \to p\bar{p}K^{+}$ e $B^{+} \to p\bar{p}\gamma K^{+}$ che presentano contributi risonanti dovuti a diversi stati di charmonium. Nel capitolo 1 è riportata una breve introduzione al Modello Standard con particolare attenzione alle sue simmetrie e alle implicazioni delle regole di conservazione che ne derivano nel contesto della teoria del quarkonium. Il capitolo 2 è invece dedicato alla descrizione di LHC e dell’apparato sperimentale di LHCb. Nel capitolo 3 è sviluppata l’analisi del decadimento $B^{+} \to \left ( c\bar{c} \right )K^{+}$ con $\left ( c\bar{c} \right ) \to p\bar{p}$, effettuata per la prima volta sui dati raccolti a partire dal 2015, confermando alcuni dei risultati dell’analisi di Run 1 recentemente pubblicata. Infine, nel capitolo 4 sono descritti gli studi preliminari del canale di decadimento $B^{+} \to p\bar{p}\gamma K^{+}$ per la ricerca del canale $B^{+} \to h_{c}K^{+}$. La strategia di analisi e la sua implementazione sono contributi originali di questo lavoro di tesi. Il capitolo conclusivo è inoltre dedicato alle prospettive future per lo studio degli stati di charmonium ricostruiti in stati finali $p\bar{p}$ e $p\bar{p}\gamma$

LHCb-Lamarr: LHCb ultra-fast simulation based on machine learning models

About 90% of the computing resources available to the LHCb experiment has been spent to produce simulated data samples for Run 2 of the Large Hadron Collider. The upgraded LHCb detector will operate at much-increased luminosity, requiring many more simulated events for the Run 3. Simulation is a key necessity of analysis to interpret data in terms of signal and background and estimate relevant efficiencies. The amount of simulation required will far exceed the pledged resources, requiring an evolution in technologies and techniques to produce simulated data samples. In this conference contribution, we discuss Lamarr, a Gaudi-based framework to speed-up the simulation production parametrizing both the detector response and the reconstruction algorithms of the LHCb experiment. Deep Generative Models powered by several algorithms and strategies are employed to effectively parameterize the high-level response of the single components of the LHCb detector, encoding within neural networks the experimental errors and uncertainties introduced in the detection and reconstruction phases. Where possible, models are trained directly on real data, statistically subtracting any background components through the application of weights. Embedding Lamarr in the general LHCb simulation framework (Gauss) allows to combine its execution with any of the available generators in a seamless way. The resulting software package enables a simulation process completely independent of the detailed simulation used to date