Speaker
Description
The current run of the Large Hadron Collider (LHC) yields on average 30-50 simultaneous pileup vertices per event, consisting of both charged and neutral showers. This number is expected to only increase at the High Luminosity LHC with predicted averages on the order of 140 pileup vertices. Pileup presents a salient problem that, if not checked, hinders the search for new physics and Standard Model precision measurements such as jet energy, jet substructure, missing momentum, and lepton isolation. While charged pileup is relatively simple to remove due to tracking information, neutral pileup remains a notable challenge for any LHC analysis. Current pileup mitigation techniques such as SoftKiller and PUPPI are all rule-based algorithms that require precise tuning, minimizing their overall flexibility. More recent work has focused on developing automated mitigation frameworks using machine learning. This study builds upon one such technique known as Training Optimal Transport using Attention Learning (TOTAL). The TOTAL methodology leverages a transformer architecture with an optimal-transport loss function to robustly learn an accurate description of pileup as a transport function by comparing matched samples with and without pileup interactions present, all without any need for assumptions of pileup nature. While TOTAL has already been shown to outperform conventional rule-based approaches, the extent of its potential performance remained unclear. In this work, we test the performance limits of TOTAL in two directions: 1) assessing the impacts of different configurations of input information and 2) testing new OT-based loss functions. Both directions have shown significant improvement over the original TOTAL methodology in terms of reconstructing relevant observables such as dijet mass for several BSM physics scenarios and a wide range of pileup conditions scaling up to 200 pileup vertices. These improvements further demonstrate the inherent value of using TOTAL as a new baseline for ML-based pileup mitigation.