Development and firmware implementation of a Machine Learning based hadronic Tau lepton Level-1 Trigger algorithm in CMS for the HL-LHC

The High-Luminosity LHC (HL-LHC) will open an unprecedented window on the weak-scale nature of the universe, providing high-precision measurements of the standard model as well as searches for new physics beyond it. The CMS Collaboration is planning to replace entirely its trigger and data acquisition systems to match this ambitious physics program. Efficiently collecting datasets in Phase-2 will be a challenging task, given the harsh environment of 200 simultaneous proton-proton interactions per HL-LHC bunch crossing. The already challenging implementation of an efficient tau lepton trigger will become, in such conditions, an even more crucial and harder task; especially interesting will be the case of hadronically decaying tau. To this end, the highly upgraded capabilities of the Phase 2 Level-1 triggering system can be exploited to design new complex machine learning based algorithms that are not yet implementable in the current Phase-1 system. Moreover, the foreseen high-granularity endcap calorimeter and the astonishing amount of information it will provide play a key role in the design of novel tau lepton triggering methods. In these proceedings, the development of a Level-1 trigger algorithm, with consistent barrel and endcap treatment, for hadronically decaying tau based on the calorimetric information from the ECAL, HCAL, and HGCAL detectors will be presented: the TauMinator. A completely new and innovative design for a Level-1 trigger algorithm based on convolutional neural networks will be shown alongside its preliminary FPGA firmware implementation. The Level-1 trigger latency and resource availability constraints will also be discussed, and their role in the algorithm design will be highlighted