Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
(2020) Journal of Instrumentation — Vol. 15, p. P06005 (2020)
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- Abstract
- Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The identification performances of a variety of algorithms are characterized in simulated events and directly compared with data. The algorithms are validated using proton-proton collision data at &surd;s = 13TeV, corresponding to an integrated luminosity of 35.9 fb−1. Systematic uncertainties are assessed by comparing the results obtained using simulation and collision data. The new techniques studied in this paper provide significant performance improvements over non-ML techniques, reducing the background rate by up to an order of magnitude at the same signal efficiency.
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Citations
Bondu, O., Bruno, G., Caputo, C., David, P., Delaere, C., Delcourt, M., Giammanco, A., Lemaitre, V., Prisciandaro, J., Saggio, A., Vidal Marono, M., Vischia, P., Zobec, J., CMS Collaboration, & et al. (2020). Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques. Journal of Instrumentation, 15, P06005. https://doi.org/10.1088/1748-0221/15/06/P06005 (Original work published 2020)