The OPERA experiment was designed to study νμ→ντ oscillations in the appearance mode in the CERN to Gran Sasso Neutrino beam (CNGS). In this Letter, we report the final analysis of the full data sample collected between 2008 and 2012, corresponding to 17.97×1019 protons on target. Selection criteria looser than in previous analyses have produced ten ντ candidate events, thus reducing the statistical uncertainty in the measurement of the oscillation parameters and of ντ properties. A multivariate approach for event identification has been applied to the candidate events and the discovery of ντ appearance is confirmed with an improved significance level of 6.1σ. |Δm232| has been measured, in appearance mode, with an accuracy of 20%. The measurement of the ντ charged-current cross section, for the first time with a negligible contamination from ¯ντ, and the first direct evidence for the ντ lepton number are also reported.
One of the most challenging data analysis tasks of modern High Energy Physics experiments is the identification of particles. In this proceedings we review the new approaches used for particle identification at the LHCb experiment. Machine-Learning based techniques are used to identify the species of charged and neutral particles using several observables obtained by the LHCb sub-detectors. We show the performances of various solutions based on Neural Network and Boosted Decision Tree models.