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.
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Acknowledgments
We acknowledge support from CERN and LHCb and from the national agency: MEiN and National Science Centre (Poland) UMO-2018/31/B /ST2/03998. The work was also supported by the Faculty of Physics and Applied Computer Science AGH UST statutory tasks within subsidy of MEiN.
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Gołaszewski, G., Kulczycki, P., Szumlak, T., Łukasik, S. (2021). Reconstruction of Long-Lived Particles in LHCb CERN Project by Data Analysis and Computational Intelligence Methods. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12742. Springer, Cham. https://doi.org/10.1007/978-3-030-77961-0_25
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DOI: https://doi.org/10.1007/978-3-030-77961-0_25
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