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Register a new userMachine Learning approach to boosting neutral particles identification in the LHCb calorimeter
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We present a new approach to identification of boosted neutral particles using Electromagnetic Calorimeter (ECAL) of the LHCb detector. The identification of photons and neutral pions is currently based on the geometric parameters which characterise the expected shape of energy deposition in the calorimeter. This allows to distinguish single photons in the electromagnetic calorimeter from overlapping photons produced from high momentum $pi^0$ decays. The novel approach proposed here is based on applying machine learning techniques to primary calorimeter information, that are energies collected in individual cells around the energy cluster. This method allows to improve separation performance of photons and neutral pions and has no significant energy dependence.
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The performance of the muon identification in LHCb is extracted from data using muons and hadrons produced in J/psi->mumu, Lambda->ppi and D^{star}->pi D0(Kpi) decays. The muon identification procedure is based on the pattern of hits in the muon chambers. A momentum dependent binary requirement is used to reduce the probability of hadrons to be misidentified as muons to the level of 1%, keeping the muon efficiency in the range of 95-98%. As further refinement, a likelihood is built for the muon and non-muon hypotheses. Adding a requirement on this likelihood that provides a total muon efficiency at the level of 93%, the hadron misidentification rates are below 0.6%.
The CALICE Semi-Digital Hadronic CALorimeter (SDHCAL) prototype using Glass Resistive Plate Chambers as a sensitive medium is the first technological prototype of a family of high-granularity calorimeters developed by the CALICE collaboration to equip the experiments of future leptonic colliders. It was exposed to beams of hadrons, electrons and muons several times in the CERN PS and SPS beamlines between 2012 and 2018. We present here a new method of particle identification within the SDHCAL using the Boosted Decision Trees (BDT) method applied to the data collected in 2015. The performance of the method is tested first with Geant4-based simulated events and then on the data collected by the SDHCAL in the energy range between 10 and 80~GeV with 10~GeV energy steps. The BDT method is then used to reject the electrons and muons that contaminate the SPS hadron beams.
We present a study of showers initiated by electrons, pions, kaons, and protons with momenta from 15 GeV to 150 GeV in the highly granular CALICE scintillator-tungsten analogue hadronic calorimeter. The data were recorded at the CERN Super Proton Synchrotron in 2011. The analysis includes measurements of the calorimeter response to each particle type as well as measurements of the energy resolution and studies of the longitudinal and radial shower development for selected particles. The results are compared to Geant4 simulations (version 9.6.p02). In the study of the energy resolution we include previously published data with beam momenta from 1 GeV to 10 GeV recorded at the CERN Proton Synchrotron in 2010.
The paper describes a method of the charged particle identification, developed for the mbox{CMD-3} detector, installed at the VEPP-2000 $e^{+}e^{-}$ collider. The method is based on the application of the boosted decision trees classifiers, trained for the optimal separation of electrons, muons, pions and kaons in the momentum range from 100 to $1200~{rm MeV}/c$. The input variables for the classifiers are linear combinations of the energy depositions of charged particles in 12 layers of the liquid xenon calorimeter of the mbox{CMD-3}. The event samples for training of the classifiers are taken from the simulation. Various issues of the detector response tuning in simulation and calibration of the calorimeter strip channels are considered. Application of the method is illustrated by the examples of separation of the $e^+e^-(gamma)$ and $pi^+pi^-(gamma)$ final states and of selection of the $K^+K^-$ final state at high energies.
Finding tracks downstream of the magnet at the earliest LHCb trigger level is not part of the baseline plan of the upgrade trigger, on account of the significant CPU time required to execute the search. Many long-lived particles, such as $K^0_S$ and strange baryons, decay after the vertex track detector, so that their reconstruction efficiency is limited. We present a study of the performance of a future innovative real-time tracking system based on FPGAs, developed within a R&D effort in the context of the LHCb Upgrade Ib (LHC Run~4), dedicated to the reconstruction of the particles downstream of the magnet in the forward tracking detector (Scintillating Fibre Tracker), that is capable of processing events at the full LHC collision rate of 30 MHz.
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