Deep sets and event-level maximum-likelihood estimation for fast pile-up jet rejection in ATLAS
This addresses the challenge of handling increased pile-up rates in particle physics experiments, particularly for multi-jet signatures, with an incremental improvement in trigger-level efficiency.
The paper tackled the problem of efficiently rejecting pile-up jets in high-luminosity proton-proton collisions at the LHC by introducing DIPz, a Deep Sets-based model for jet origin regression, combined with an event-level discriminant (MLPL), resulting in a robust and computationally efficient method for real-time event selection in ATLAS.
Multiple proton-proton collisions (pile-up) occur at every bunch crossing at the LHC, with the mean number of interactions expected to reach 80 during Run 3 and up to 200 at the High-Luminosity LHC. As a direct consequence, events with multijet signatures will occur at increasingly high rates. To cope with the increased luminosity, being able to efficiently group jets according to their origin along the beamline is crucial, particularly at the trigger level. In this work, a novel uncertainty-aware jet regression model based on a Deep Sets architecture is introduced, DIPz, to regress on a jet origin position along the beamline. The inputs to the DIPz algorithm are the charged particle tracks associated to each jet. An event-level discriminant, the Maximum Log Product of Likelihoods (MLPL), is constructed by combining the DIPz per-jet predictions. MLPL is cut-optimized to select events compatible with targeted multi-jet signature selection. This combined approach provides a robust and computationally efficient method for pile-up rejection in multi-jet final states, applicable to real-time event selections at the ATLAS High Level Trigger.