Search Menu
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information
Search Scholarly Publications

Title: JEDI-net: a jet identification algorithm based on interaction networks

Journal Article · · European Physical Journal. C, Particles and Fields
 [1];  [1]; ORCiD logo [2];  [1];  [1];  [1];  [3];  [1];  [1];  [1]
  1. California Inst. of Technology (CalTech), Pasadena, CA (United States)
  2. Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States); Univ. of California, San Diego, CA (United States)
  3. European Organization for Nuclear Research (CERN), Geneva (Switzerland)
+ Show Author Affiliations

We investigate the performance of a jet identification algorithm based on interaction networks (JEDI-net) to identify all-hadronic decays of high-momentum heavy particles produced at the LHC and distinguish them from ordinary jets originating from the hadronization of quarks and gluons. The jet dynamics are described as a set of one-to-one interactions between the jet constituents. Based on a representation learned from these interactions, the jet is associated to one of the considered categories. Unlike other architectures, the JEDI-net models achieve their performance without special handling of the sparse input jet representation, extensive pre-processing, particle ordering, or specific assumptions regarding the underlying detector geometry. The presented models give better results with less model parameters, offering interesting prospects for LHC applications.

Research Organization:
Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
Sponsoring Organization:
USDOE Office of Science (SC), High Energy Physics (HEP)
Grant/Contract Number:
AC02-07CH11359; SC0011925
OSTI ID:
1619359
Alternate ID(s):
OSTI ID: 1561543
Report Number(s):
arXiv:1908.05318; FERMILAB-PUB-19-360-PPD; oai:inspirehep.net:1749751
Journal Information:
European Physical Journal. C, Particles and Fields, Vol. 80, Issue 1; ISSN 1434-6044
Publisher:
SpringerCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 60 works
Citation information provided by
Web of Science

References (54)

HLS4ML LHC Jet dataset (150 particles) dataset January 2020
The anti- k t jet clustering algorithm journal April 2008
Pulling out all the tops with computer vision and deep learning journal October 2018
Learning representations of irregular particle-detector geometry with distance-weighted graph networks journal July 2019
Deep-learned Top Tagging with a Lorentz Layer journal January 2018
Fast inference of deep neural networks in FPGAs for particle physics journal July 2018
Energy flow networks: deep sets for particle jets journal January 2019
HLS4ML LHC Jet dataset (30 particles) dataset January 2020
Longitudinally-invariant k⊥-clustering algorithms for hadron-hadron collisions journal September 1993
Graph Neural Networks for IceCube Signal Classification conference December 2018
The Machine Learning landscape of top taggers journal January 2019
Jet-images — deep learning edition journal July 2016
Deep-learning top taggers or the end of QCD? journal May 2017
Deep learning in color: towards automated quark/gluon jet discrimination journal January 2017
FastJet user manual: (for version 3.0.2) journal March 2012
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position journal April 1980
Novel jet observables from machine learning journal March 2018
Identifying boosted objects with N-subjettiness journal March 2011
Jet flavor classification in high-energy physics with deep neural networks journal December 2016
Probing stop pair production at the LHC with graph neural networks journal August 2019
Recursive Neural Networks in Quark/Gluon Tagging journal June 2018
Image Processing, Computer Vision, and Deep Learning: new approaches to the analysis and physics interpretation of LHC events journal October 2016
Pileup mitigation at the Large Hadron Collider with graph neural networks journal July 2019
QCD-aware recursive neural networks for jet physics journal January 2019
HLS4ML LHC Jet dataset (50 particles) dataset January 2020
Principles of Neurodynamics. Perceptrons and the Theory of Brain Mechanisms. journal September 1962
Better jet clustering algorithms journal August 1997
The importance of calorimetry for highly-boosted jet substructure journal January 2018
HLS4ML LHC Jet dataset (100 particles) dataset January 2020
Phoneme recognition using time-delay neural networks journal March 1989
jmduarte/JEDInet-code: v0.1.0 software August 2020
Deep-learned Top Tagging with a Lorentz Layer text January 2018
HLS4ML LHC Jet dataset (50 particles) dataset January 2020
HLS4ML LHC Jet dataset (100 particles) dataset January 2020
HLS4ML LHC Jet dataset (150 particles) dataset January 2020
The Machine Learning landscape of top taggers text January 2019
Deep-learning top taggers or the end of QCD text January 2017
HLS4ML LHC Jet dataset (30 particles) dataset January 2020
The anti-k_t jet clustering algorithm text January 2008
Deep learning in color: towards automated quark/gluon jet discrimination text January 2016
Deep-learning Top Taggers or The End of QCD? text January 2017
QCD-Aware Recursive Neural Networks for Jet Physics text January 2017
Recursive Neural Networks in Quark/Gluon Tagging text January 2017
Energy Flow Networks: Deep Sets for Particle Jets text January 2018
Pileup mitigation at the Large Hadron Collider with Graph Neural Networks preprint January 2018
Better Jet Clustering Algorithms text January 1997
Principles of Neurodynamics. Perceptrons and the Theory of Brain Mechanisms. journal September 1962
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) preprint January 2015
Self-Normalizing Neural Networks text January 2017
HLS4ML LHC Jet dataset (30 particles) dataset January 2020
HLS4ML LHC Jet dataset (50 particles) dataset January 2020
HLS4ML LHC Jet dataset (100 particles) dataset January 2020
HLS4ML LHC Jet dataset (150 particles) dataset January 2020
jmduarte/JEDInet-code: v0.1.0 software August 2020

Cited By (1)

Ps and Qs: Quantization-Aware Pruning for Efficient Low Latency Neural Network Inference journal July 2021

Similar Records

Related Subjects

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS