Identifying the quantum properties of hadronic resonances using machine learning

• DOI: 10.21468/scipostphyscore.8.2.039
• Publication Date: 2025-04-28T11:56:58Z
• Authors: Jakub Filipek; Shih-Chieh Hsu; John Kruper; Kirtimaan Mohan; Benjamin Nachman

With the great promise of deep learning, discoveries new particles at Large Hadron Collider (LHC) may be imminent. Following discovery a Beyond Standard model particle in an all-hadronic channel, learning can also used to identify its quantum numbers. Convolutional neural networks (CNNs) using jet-images significantly improve upon existing techniques chromodynamic (QCD) (‘color’) as well spin two-prong resonance substructure. Additionally, are useful determining what information jet radiation pattern is for classification, which could inspire future taggers. These categorization and important addition growing substructure toolkit, searches measurements LHC now future.