Articolo in rivista, 2022, ENG, 10.1088/1748-0221/17/07/P07023

Identification of hadronic tau lepton decays using a deep neural network

Tumasyan, A.; Adam, W.; Andrejkovic, J. W.; Bergauer, T.; Chatterjee, S.; Dragicevic, M.; Escalante Del Valle, A.; Frühwirth, R.; Jeitler, M.; Krammer, N.; Lechner, L.; Liko, D.; Mikulec, I.; Paulitsch, P.; Pitters, F. M.; Schieck, J.; Schöfbeck, R.; Schwarz, D.; Templ, S.; Waltenberger, W.; Wulz, C. E.; Chekhovsky, V.; Litomin, A.; Makarenko, V.; Darwish, M. R.; De Wolf, E. A.; Janssen, T.; Kello, T.; Lelek, A.; Rejeb Sfar, H.; Van Mechelen, P.; Van Putte, S.; Van Remortel, N.; Blekman, F.; Bols, E. S.; D'Hondt, J.; Delcourt, M.; El Faham, H.; Lowette, S.; Moortgat, S.; Morton, A.; Müller, D.; Sahasransu, A. R.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Beghin, D.; Bilin, B.; Clerbaux, B.; De Lentdecker, G.; Favart, L.; Grebenyuk, A.; Kalsi, A. K.; Lee, K.; Mahdavikhorrami, M.; Makarenko, I.; Moureaux, L.; Pétré, L.; Popov, A.; Postiau, N.; Starling, E.; Thomas, L.; Vanden Bemden, M.; Vander Velde, C.; Vanlaer, P.; Wezenbeek, L.; Cornelis, T.; Dobur, D.; Knolle, J.; Lambrecht, L.; Mestdach, G.; Niedziela, M.; Roskas, C.; Samalan, A.; Skovpen, K.; Tytgat, M.; Vermassen, B.; Vit, M.; Benecke, A.; Bethani, A.; Bruno, G.; Bury, F.; Caputo, C.; David, P.; Delaere, C.; Donertas, I. S.; Giammanco, A.; Jaffel, K.; Jain, Sa; Lemaitre, V.; Mondal, K.; Prisciandaro, J.; Taliercio, A.; Teklishyn, M.; Tran, T. T.; Vischia, P.; Wertz, S.; Alves, G. A.; Hensel, C.; Moraes, A.

Yerevan Physics Institute; Institute for Nuclear Problems of Belarusian State University; Universiteit Gent; Vrije Universiteit Brussel; Institut fur Hochenergiephysik; Technische Universität Wien; Universiteit Antwerpen; Centro Brasileiro de Pesquisas Físicas; Arab Academy for Science, Technology and Maritime Transport; Université Catholique de Louvain; Université Libre de Bruxelles

A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons (? h) that originate from genuine tau leptons in the CMS detector against ? h candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a ? h candidate and employs a deep neural network with convolutional layers to efficiently process the inputs. This algorithm leads to a significantly improved performance compared with the previously used one. For example, the efficiency for a genuine ? h to pass the discriminator against jets increases by 10-30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient ? h reconstruction is introduced that incorporates additional hadronic decay modes. The superior performance of the new algorithm to discriminate against jets, electrons, and muons and the improved ? h reconstruction method are validated with LHC proton-proton collision data at s = 13 TeV.

Journal of instrumentation 17 (7)

Keywords

calibration and fitting methods, cluster finding, Large detector systems for particle and astroparticle physics, Particle identification methods, Pattern recognition

CNR authors

Moscatelli Francesco

CNR institutes

IOM – Istituto officina dei materiali