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原文摘要

Multiple proton-proton interactions occur in the same bunch crossing at the LHC ("pile-up"), with the mean number of interactions per bunch crossing reaching up to about 60 in Run 3 and up to about 200 at the High-Luminosity LHC. As a direct consequence, hadronic activity within each bunch crossing increases, making it more difficult to identify collisions of interest pertaining to multijet final states. This work introduces DIPz, an uncertainty-aware Deep Sets-based machine-learning model that estimates each jet's origin along the beam axis as a Gaussian likelihood using the set of charged-particle tracks matched to the jet. Next, the DIPz predictions for each jet are combined into a likelihood-based event-level discriminant, which is optimised to select events compatible with a single collision producing a given jet multiplicity. This combined approach provides a robust and computationally efficient method for pile-up rejection in multijet final states, applicable to real-time event selections at the ATLAS High Level Trigger (HLT). Deployed online during Run 3 data taking, this approach reduces background rates by more than a factor of two, and decreases CPU consumption by approximately one third relative to the baseline data-taking configuration. It provides an early, flavour-agnostic pile-up jet rejection layer at the HLT preselection stage, introducing a previously unavailable handle for high-multiplicity jet signatures targeted by ATLAS.