Identifying the Quantum Properties of Hadronic Resonances using Machine Learning

With the great promise of deep learning, discoveries of new particles at the Large Hadron Collider (LHC) may be imminent. Following the discovery of a new Beyond the Standard model particle in an all-hadronic channel, deep learning can also be used to identify its quantum numbers. Convolutional neur...

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Bibliographic Details
Published inarXiv.org
Main Authors Filipek, Jakub, Hsu, Shih-Chieh, Kruper, John, Mohan, Kirtimaan, Nachman, Benjamin
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 30.10.2024
Subjects
Artificial neural networks
Deep learning
Large Hadron Collider
Machine learning
Quantum chromodynamics
Quantum numbers
Standard model (particle physics)
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Summary:With the great promise of deep learning, discoveries of new particles at the Large Hadron Collider (LHC) may be imminent. Following the discovery of a new Beyond the Standard model particle in an all-hadronic channel, deep learning can also be used to identify its quantum numbers. Convolutional neural networks (CNNs) using jet-images can significantly improve upon existing techniques to identify the quantum chromodynamic (QCD) (`color') as well as the spin of a two-prong resonance using its substructure. Additionally, jet-images are useful in determining what information in the jet radiation pattern is useful for classification, which could inspire future taggers. These techniques improve the categorization of new particles and are an important addition to the growing jet substructure toolkit, for searches and measurements at the LHC now and in the future.
ISSN:2331-8422