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T: Fachverband Teilchenphysik

T 77: Deep Learning III

T 77.6: Vortrag

Donnerstag, 28. März 2019, 17:15–17:30, H06

ttγ topology training through neural network — •Binish Batool — binish.batool@cern.ch

The study of the process of production of Top Quarks in association with Photon ( ttγ ) is done as an handle to study the electroweak coupling. It is being studied with full run2 data at the centre of mass of 13 TeV of LHC for higher precision. The usage of advance techniques is being done in this analysis which include the analysis independent approach to distinct the real prompt photon from hadron-fakes and an analysis dependent approach which employs the ttγ topology. This talk will cover the later one. This approach takes the form of neural network (NN) and its architecture is chosen to provide best suppression for signal and background. The ttγ topology for single and the dilepton channel has been implemented in NN in separate mode.