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T: Fachverband Teilchenphysik

T 57: Calorimeters I

T 57.5: Vortrag

Mittwoch, 17. März 2021, 17:00–17:15, Tg

Artificial Neural Networks for the Energy Reconstruction of ATLAS Liquid-Argon Calorimeter Signals — •Anne-Sophie Berthold, Nick Fritzsche, Wolfgang Mader, Arno Straessner, and Johann Christoph Voigt — Institut für Kern- und Teilchenphysik, Dresden, Deutschland

Starting in 2027, the enhanced performance of the High-Luminosity LHC will increase the number of particle collisions in the ATLAS detector significantly. The Phase-II upgrade of the detector aims to cope with that. Since up to 200 pile-up events will emerge within one bunch crossing, one important part of this upgrade will be the processing of the Liquid-Argon Calorimeter signals. It has been shown that the conventional signal processing, which applies an optimal filtering algorithm, will loose its performance due to the increase of overlapping signals and a trigger scheme with trigger accept signals in each LHC bunch crossing. That is why more sophisticated algorithms such as neural networks come into focus. This talk will deal with the development and performance of convolutional neural networks, which on the one hand aim to detect signals and reconstruct their energy under various conditions, and on the other hand need to satisfy resource restrictions.