Jet-images — deep learning edition

A bstract Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning architectures to identify highly boosted W bosons. Mod...

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Bibliographic Details
Published in:The journal of high energy physics 2016-07, Vol.2016 (7), p.1-32, Article 69
Main Authors: de Oliveira, Luke, Kagan, Michael, Mackey, Lester, Nachman, Benjamin, Schwartzman, Ariel
Format: Article
Language:English
Subjects:
Algorithms
Architecture
Classical and Quantum Gravitation
Collimation
Detectors
Elementary Particles
hadron-hadron scattering (experiments)
High energy physics
jet substructure
Jets
Learning
MATHEMATICS AND COMPUTING
Networks
Physics
Physics and Astronomy
PHYSICS OF ELEMENTARY PARTICLES AND FIELDS
Quantum Field Theories
Quantum Field Theory
Quantum Physics
Regular Article - Experimental Physics
Relativity Theory
String Theory
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Summary:A bstract Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning architectures to identify highly boosted W bosons. Modern deep learning algorithms trained on jet images can out-perform standard physically-motivated feature driven approaches to jet tagging. We develop techniques for visualizing how these features are learned by the network and what additional information is used to improve performance. This interplay between physicallymotivated feature driven tools and supervised learning algorithms is general and can be used to significantly increase the sensitivity to discover new particles and new forces, and gain a deeper understanding of the physics within jets.
ISSN:1029-8479
1029-8479
DOI:10.1007/JHEP07(2016)069