SMuK 2023 – wissenschaftliches Programm
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T: Fachverband Teilchenphysik
T 48: Exp. Methods I
T 48.1: Vortrag
Dienstag, 21. März 2023, 17:00–17:15, WIL/C129
Tau-lepton decay mode classification using machine learning in ATLAS — •Jonathan Pampel1, Duc Bao Ta2, Christina Dimitriadi1, Jochen Dingfelder1, Tatjana Lenz1, and Eckhard von Törne1 — 1University of Bonn, Germany — 2University of Mainz, Germany
The tau-lepton is the heaviest charged lepton with a mass of about twice the mass of the proton. It can decay leptonically into a neutrino and other leptons or hadronically into a neutrino and hadrons, the latter being mostly pions. In the ATLAS collaboration at CERN, there are already several algorithms for the decay mode classification of hadronically decaying tau-leptons (tau-jets).
This talk presents a novel technique based on convolutional neural networks to classify the hadronic tau-lepton decay modes. The goal is to count the number of neutral and charged pions in a tau-jet using calorimeter information. To do this, for each calorimeter layer, a `picture' of the tau-jet is generated. These `pictures' are used as input for a neural network built from several 2D convolution and pooling layers and flattening layer followed by a number of dense layers.
The preliminary results of this study will be presented based on ATLAS Run 2 Monte Carlo samples, i.e. pp-collisions at a center of mass energy of 13TeV. This includes an introduction into the problem as well as a visualization of the preprocessed data which is fed into the neural network. Finally, the best performing neural network's architecture and its performance will be presented.