Using deep learning for particle identification and energy estimation in CMS HGCAL L1 trigger

In run 4 of the LHC, the extreme high luminosity is expected to generate an enormous pileup of up to 200 
proton-proton collisions for each bunch crossing. This has to be read out at 750 kHz with a maximum 
latency of  12.5𝜇s. In order to disentangle the energy from pileup collision, the upgraded CMS detector for 
Run-4 will feature a new High Granularity Calorimeter (HGCAL) with unprecedented lateral and 
longitudinal segmentation. The total number of channels read out into the Level-1 trigger processor will be 
of the order of 106. To process this data with such small latency, we need to develop sophisticated 
algorithms. In this report, we aim to use machine learning techniques for electron-photon identification and 
energy estimation in the L1 Trigger. The idea is to implement the architectures on FPGA boards that will 
have fast inference, enough to cope with the requirements of the HGCAL.