Optimal transport for a novel event description at hadron colliders

Abstract

We propose a novel strategy for disentangling proton collisions at hadron colliders such as the LHC that considerably improves over the current state of the art. Employing a metric inspired by optimal transport problems as the cost function of a graph neural network, our algorithm is able to compare two particle collections with different noise levels and learns to flag particles originating from the main interaction amidst products from up to 200 simultaneous pileup collisions. We thereby sidestep the critical task of obtaining a ground truth by labeling particles and avoid arduous human annotation in favor of labels derived in situ through a self-supervised process. We demonstrate how our approach - which, unlike competing algorithms, is trivial to implement - improves the resolution in key objects used in precision measurements and searches alike and present large sensitivity gains in searching for exotic Higgs boson decays at the High-Luminosity LHC.

12 pages, 5 figures

References in corpus (9)

• An Introduction to PYTHIA 8.2
• Particle-flow reconstruction and global event description with the CMS detector
• Pileup Per Particle Identification
• Interleaved Parton Showers and Tuning Prospects
• Pileup Mitigation with Machine Learning (PUMML)
• Search for invisible Higgs-boson decays in events with vector-boson fusion signatures using 139 ${\text{fb}}^{-1}$ of proton-proton data recorded by the ATLAS experiment
• Search for invisible decays of the Higgs boson produced via vector boson fusion in proton-proton collisions at $\sqrt{s}=$ 13 TeV
• Pile-Up Mitigation using Attention
• Semi-supervised Graph Neural Networks for Pileup Noise Removal

Cited by in corpus (1)

• Distilling particle knowledge for fast reconstruction at high-energy physics experiments