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T: Fachverband Teilchenphysik

T 88: Top quarks: associated production

T 88.5: Vortrag

Donnerstag, 2. April 2020, 17:30–17:45, L-4.001

Separation of Signal and Background in ttγ Processes using Deep Neural Networks in Single Lepton Final States at √s = 13 TeV in ATLAS — •Steffen Korn, Thomas Peiffer, Arnulf Quadt, Elizaveta Shabalina, and Knut Zoch — II. Physikalisches Institut, Georg-August-Universität Göttingen

Through the associated production of the ttγ process, the strength of the electromagnetic coupling of the top quark and the photon can be measured. The measurement of this fundamental parameter of the Standard Model (SM) also serves as a probe to new physics beyond the SM. First evidence for this process was found by CDF at the Tevatron at √s = 1.96 TeV. The process was later observed by ATLAS and CMS at √s = 7 and 8 TeV with increased precision. Due to the similar topology between signal and background processes and a signal to background ratio of approximately 1:1 in the single lepton channel, deep neural networks (DNN) are used to improve the separation of signal and background processes. The separation of ttγ signal processes from background processes in proton-proton collisions data, taken be- tween 2015 and 2018 with the ATLAS detector, is presented. Signal and background processes are hereby grouped into multiple different classes using a deep multi-class neural network. The performance of different DNN architectures based on a one-vs-one and a one-vs-many training approach and their effect on the event selection and the sensitivity of the analysis is presented.