Published 2016 | Version v1
Journal article

Boosted Jet Tagging with Jet-Images and Deep Neural Networks

  • 1. SLAC National Accelerator Laboratory, Menlo Park, CA (United States)
  • 2. Stanford University, Stanford, CA (United States)

Description

Building on the jet-image based representation of high energy jets, we develop computer vision based techniques for jet tagging through the use of deep neural networks. Jet-images enabled the connection between jet substructure and tagging with the fields of computer vision and image processing. We show how applying such techniques using deep neural networks can improve the performance to identify highly boosted W bosons with respect to state-of-the-art substructure methods. In addition, we explore new ways to extract and visualize the discriminating features of different classes of jets, adding a new capability to understand the physics within jets and to design more powerful jet tagging methods

Availability note (English)

Available from http://www.epj-conferences.org/articles/epjconf/pdf/2016/22/epjconf_dots2016_00009.pdf; https://doaj.org/article/27e85f8a41024a9894fb8cdda6952361; http://dx.doi.org/10.1051/epjconf/201612700009

Additional details

Publishing Information

Journal Title
EPJ. Web of Conferences
Journal Volume
127
Journal Page Range
00009 p.
ISSN
2100-014X

Conference

Title
Connecting the Dots
Dates
22-24 Feb 2016
Place
Vienna (Austria)

INIS

Country of Publication
France
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
48007605
Subject category
S72: PHYSICS OF ELEMENTARY PARTICLES AND FIELDS;
Resource subtype / Literary indicator
Conference
Descriptors DEI
IMAGE PROCESSING; JET MODEL; NEURAL NETWORKS; PARTICLE IDENTIFICATION; W MINUS BOSONS; W PLUS BOSONS
Descriptors DEC
BOSONS; ELEMENTARY PARTICLES; INTERMEDIATE BOSONS; INTERMEDIATE VECTOR BOSONS; MATHEMATICAL MODELS; PARTICLE MODELS; PROCESSING

Optional Information

Copyright
Copyright (c) 2016 The Authors. Published by EDP Sciences