Particle Physics Seminar: Neural networks for boosted di-τ identification

Abstract:

1. We train several neural networks and boosted decision trees to discriminate fully-hadronic boosted di-τ topologies against background QCD jets, using calorimeter and tracking information. Boosted di-τ topologies consisting of a pair of highly collimated τ-leptons, arise from the decay of a highly energetic Standard Model Higgs or Z boson or from particles beyond the Standard Model. We compare the tagging performance for different neural-network models and a boosted decision tree, the latter serving as a simple benchmark machine learning model. 2. I will discuss the signal modelling in the context of a search for diphoton resonances in the H->aa->gamma gamma tau_had tau_had channel. Our search uses proton-proton collision data from the Large Hadron Collider at a center-of-mass energy of 13 TeV recorded with the ATLAS detector during the Run 2 of the LHC from 2015 to 2018, corresponding to a total integrated luminosity of 138 fb-1. The mass range for our search is from 10 GeV to 60 GeV. I will present the analysis strategy, focusing on the signal modelling part. In particular, I will describe the extraction of the signal shape and its validation through signal injection tests.

Seminar Organizer: Dr. Michael Geller