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ICHEP / Particle identification at LHCb: new calibration techniques and machine learning classification algorithms (564)

Particle identification (PID) plays a crucial role in LHCb analyses. Combining information from LHCb subdetectors allows one to distinguish between various species of long-lived charged and neutral particles. PID performance directly affects the sensitivity of most LHCb measurements. Advanced multivariate approaches are used at LHCb to obtain the best PID performance and control systematic uncertainties. This talk highlights recent developments in PID that use innovative machine learning techniques, as well as novel data-driven approaches which ensure that PID performance is well reproduced in simulation. PID strategy and performance at LHCb in Run 2 The LHCb particle identification (PID) system is composed of two ring-imaging Cherenkov detectors , a series of muon chambers and a calorimeter system. A novel strategy has been introduced in Run 2, where the selection of PID calibration samples for charged particles and neutrals is implemented in the LHCb software trigger. A further processing of the data is required in order to provide samples for the determination of PID performance, which is achieved through a centralised production that makes highly efficient use of computing resources. This talk covers the major steps of the implementation, and highlights the PID performance achieved in Run 2. submitted to Computing and Data Handling

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