Deep learning to improve the sensitivity of Di-Higgs searches in the 4b channel

• Published in: The journal of high energy physics 2024-09, Vol.2024 (9), p.139-25, Article 139
• Main Authors: Chiang, Cheng-Wei, Hsieh, Feng-Yang, Hsu, Shih-Chieh, Low, Ian
• Format: Article
• Language: English
• Subjects: Algorithms; Classical and Quantum Gravitation; Deep learning; Elementary Particles; Higgs bosons; Higgs Production; Higgs Properties; Large Hadron Collider; Luminosity; Machine learning; Neural networks; Particle decay; Physics; Physics and Astronomy; Quantum Field Theories; Quantum Field Theory; Quantum Physics; Regular Article - Theoretical Physics; Relativity Theory; String Theory
• ISSN: 1029-8479; 1029-8479
• DOI: 10.1007/JHEP09(2024)139

A bstract The study of di-Higgs events, both resonant and non-resonant, plays a crucial role in understanding the fundamental interactions of the Higgs boson. In this work we consider di-Higgs events decaying into four b -quarks and propose to improve the experimental sensitivity by utilizing a novel machine learning algorithm known as Symmetry Preserving Attention Network (S pa -N et ) — a neural network structure whose architecture is designed to incorporate the inherent symmetries in particle reconstruction tasks. We demonstrate that the S pa -N et can enhance the experimental reach over baseline methods such as the cut-based and the Dense Neural Network-based analyses. At the Large Hadron Collider, with a 14-TeV center-of-mass energy and an integrated luminosity of 300 fb −1 , the S pa -N et allows us to establish 95% C.L. upper limits in resonant production cross-sections that are 10% to 45% stronger than baseline methods. For non-resonant di-Higgs production, S pa -N et enables us to constrain the self-coupling that is 9% more stringent than the baseline method.