29 July 2019 to 2 August 2019
Northeastern University
US/Eastern timezone

Identifying the Quantum Color Representation of New Particles with Machine Learning

29 Jul 2019, 14:45
17m
Shillman 335 (Northeastern University)

Shillman 335

Northeastern University

Oral Presentation Beyond Standard Model Physics Beyond Standard Model

Speaker

John Alexander Kruper (University of Washington (US))

Description

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 particle in an all-hadronic channel, deep learning can also be used to identify the quantum numbers of the new particle. We show that convolutional neural networks (CNNs) using jet images can significantly improve upon existing techniques to identify the quantum chromodynamic (QCD) representation (`color’) of a two-prong jet using its substructure. In addition to demonstrating the capabilities of CNNs for quantum color tagging, we study what information in the jet radiation pattern is useful for classification. 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.

Primary authors

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

Presentation Materials