Jul 6 – 8, 2021
Europe/Zurich timezone

Identifying the Quantum Properties of Hadronic Resonances using Machine Learning

Jul 7, 2021, 10:20 AM


Jakub Filipek (University of Washington)


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.

Affiliation University of Washington
Academic Rank Master's Student

Primary authors

Ben Nachman (Lawrence Berkeley National Lab. (US)) Jakub Filipek (University of Washington) John Kruper (University of Washington) Kirtimaan Mohan Shih-Chieh Hsu (University of Washington Seattle (US))

Presentation materials

There are no materials yet.