Reconstruction of Long-Lived Particles in LHCb CERN Project by Data Analysis and Computational Intelligence Methods

Grzegorz Golaszewski, Piotr Kulczycki, Tomasz Szumlak, Szymon Lukasik

Abstract: LHCb at CERN, Geneva is a world-leading high energy physics experiment dedicated to searching for New Physics phenomena. The experiment is undergoing a major upgrade and will rely entirely on a flexible software trigger to process the data in real-time. In this paper a novel approach to reconstructing (detecting) long-lived particles using a new pattern matching procedure is presented. A large simulated data sample is applied to build an initial track pattern by an unsupervised approach. The pattern is then updated and verified by real collision data. As a performance index, the difference between density estimated by nonparametric methods using experimental streaming data and the one based on theoretical premises is used. Fuzzy clustering methods are applied for a pattern size reduction. A final decision is made in a real-time regime with rigorous time boundaries.