Deep learning in color: towards automated quark/gluon jet discrimination

DOI10.1007/JHEP01(2017)110zbMath1373.81388arXiv1612.01551MaRDI QIDQ1678915

Eric M. Metodiev, Patrick T. Komiske, Matthew D. Schwartz

Publication date: 7 November 2017

Published in: Journal of High Energy Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1612.01551

zbMATH Keywords

jets

Mathematics Subject Classification ID

Strong interaction, including quantum chromodynamics (81V05)

Related Items (7)

Hierarchical clustering with deep q-learning ⋮ Power counting energy flow polynomials ⋮ Weakly supervised classification in high energy physics ⋮ Supervised Deep Learning in High Energy Phenomenology: a Mini Review* ⋮ Learning to inflate. A gradient ascent approach to random inflation ⋮ An efficient Lorentz equivariant graph neural network for jet tagging ⋮ Replica symmetry breaking in neural networks: a few steps toward rigorous results

Uses Software

• Theano
• Scikit
• PYTHIA8
• FastJet
• Herwig++
• Keras
• Spearmint
• ImageNet
• Adam
• AlexNet

Cites Work

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• Energy correlation functions for jet substructure
• An introduction to PYTHIA 8.2
• A determination of parton distributions with faithful uncertainty estimation
• FastJet user manual (for version 3.0.2)
• Multilayer feedforward networks are universal approximators
• The anti-\(k_t\) jet clustering algorithm

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