Elena Ferro
Implementation of Deep Neural Networks for the Level 1 Trigger system of the future High-Granularity Calorimeter (HGCAL).
Rel. Guido Masera, Jean-Baptiste Sauvan. Politecnico di Torino, Corso di laurea magistrale in Nanotechnologies For Icts (Nanotecnologie Per Le Ict), 2020
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Abstract
The CMS detector (Compact Muon Solenoid) is a general-purpose particle detector recording the output of proton-proton collisions at the CERN Large Hadron Collider (LHC). It has been recording collision data since 2010 and has led to the discovery of the Higgs boson in 2012, together with the ATLAS detector. In 2026 the LHC will enter a new phase, the high-luminosity phase (HL-LHC). For the HL-LHC, the end-cap calorimeters of the CMS detector will be replaced by a new radiation-resistant and highly granular calorimeter (HGCAL). The high granularity of this detector poses severe challenges on the trigger system of the experiment, a two-stage online system which selects collision events of interest to be recorded.
The first stage (L1 trigger) is based on custom FPGAs and takes as input data coming from the detector at an event rate of 40MHz
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