Pile-up mitigation using attention. Issue 2 (1st June 2022)

• Record Type: Journal Article
• Authors: Maier, B; Narayanan, S M; de Castro, G; Goncharov, M; Paus, Ch; Schott, M
• Is Part Of: Machine learning: science and technology. Volume 3:Issue 2(2022)
• Publication Date: 2022-06-01
• Subjects: LHC -- HL-LHC -- pile-up -- transformers -- machine learning; 006.31
• DOI: 10.1088/2632-2153/ac7198
• Languages: English
• ISSNs: 2632-2153

Abstract

Abstract: Particle production from secondary proton-proton collisions, commonly referred to as pile-up, impair the sensitivity of both new physics searches and precision measurements at large hadron collider (LHC) experiments. We propose a novel algorithm, Puma, for modeling pile-up with the help of deep neural networks based on sparse transformers. These attention mechanisms were developed for natural language processing but have become popular in other applications. In a realistic detector simulation, our method outperforms classical benchmark algorithms for pile-up mitigation in key observables. It provides a perspective for mitigating the effects of pile-up in the high luminosity era of the LHC, where up to 200 proton-proton collisions are expected to occur simultaneously.