LHCb is a particle physics experiment specialised in the study of properties and decays of heavy particles containing beauty and charm quarks, created in proton–proton collisions at the LHC. In Run 3, LHCb will collect physics events at higher rates thanks to newly installed detectors and a revolutionary software trigger that will enable LHCb to rapidly process signal data. The increase in physics reach brought by this upgrade comes at the cost of having to discriminate the interesting particles in the event from the rest at trigger time, since the amount of data produced would be too big to be stored. The higher collision rates also bring a much larger particle-combinatorics challenge than before, which motivates the development of new trigger approaches and algorithms. The current challenges will become dramatic for the foreseen Upgrade II of LHCb, in which the expected number of proton-proton collisions per event will experience an extra ten-fold increase.
The EU-funded LHCbDFEI project has led to the design and development of the prototype for a new trigger algorithm, that performs a Deep-learning based Full Event Interpretation (DFEI) for the first time in LHCb. The DFEI algorithm provides an inclusive, automatic and accurate multi-signal selection per event, which potentially maximises the trigger efficiency that can be achieved. This has been made possible by systematically leveraging the correlations amongst all the reconstructed particles per event, thanks to the use of Graph Neural Networks.
As a secondary complementary goal of the project, the analysis of LHCb data was done towards the measurement of quantum amplitudes of beauty-hadron decays with a tau lepton, that has important consequences for tests of the Standard Model of particle physics.
