Fetching the paper…

Reading the bibliography…

2019

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

Moreno, Eric A., Cerri, Olmo, Duarte, Javier M. et al.

Original
PDF
Post on X

Understand

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.

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

Built on

A. Butter et al., “The Machine Learning Landscape of Top Taggers”, SciPost Phys
Original
1902
Earlier work this paper cites.
H. Qu and L. Gouskos, “ParticleNet: Jet Tagging via Particle Clouds”, arXiv:1902.08570
Original
1902
Earlier work this paper cites.
S. R. Qasim, J. Kieseler, Y. Iiyama, and M. Pierini, “Learning representations of irregular particle-detector geometry with distance-weighted graph networks”, Eur. Phys. J
Original
1902
Earlier work this paper cites.
J. Orbach, “Principles of Neurodynamics. Perceptrons and the Theory of Brain Mechanisms.”, Archives of General Psychiatry
1962
Earlier work this paper cites.
K. Fukushima, “Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position”, Biological Cybernetics
1980
Earlier work this paper cites.
Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1990
Y. L. Cun et al., “Advances in Neural Information Processing Systems 2”, ch. Handwritten Digit Recognition with a Back-propagation Network, pp. 396–404 · 1990
Earlier work this paper cites.
Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1990
A. Waibel et al., “Readings in Speech Recognition”, ch. Phoneme Recognition Using Time-delay Neural Networks, pp. 393–404 · 1990
Earlier work this paper cites.
Morgan-Kaufmann, 1990
Y. LeCun, J. S. Denker, and S. A. Solla, “Optimal Brain Damage”, in Advances in Neural Information Processing Systems 2 · 1990
Earlier work this paper cites.
S. Catani, Y. L. Dokshitzer, M. H. Seymour, and B. R. Webber, “Longitudinally invariant K t K_{t} clustering algorithms for hadron hadron collisions”, Nucl. Phys. B
1993
Earlier work this paper cites.
Y. L. Dokshitzer, G. D. Leder, S. Moretti, and B. R. Webber, “Better jet clustering algorithms”, JHEP
1997
Earlier work this paper cites.
M. Cacciari, G. P. Salam, and G. Soyez, “The anti- k t k_{t} jet clustering algorithm”, JHEP
Original
2008
Earlier work this paper cites.
06, 2010
V. Nair and G. E. Hinton, “Rectified linear units improve restricted Boltzmann machines”, in Proceedings of ICML · 2010
Earlier work this paper cites.
J. Thaler and K. Van Tilburg, “Identifying Boosted Objects with N-subjettiness”, JHEP
Original
2011
Earlier work this paper cites.
M. Cacciari, G. P. Salam, and G. Soyez, “FastJet user manual”, Eur. Phys. J. C
Original
2012
Earlier work this paper cites.
GPy, “GPy: A Gaussian process framework in python”. http://github.com/SheffieldML/GPy
2012
Earlier work this paper cites.
M. D. Zeiler, “ADADELTA: An Adaptive Learning Rate Method”, CoRR
Original
2012
Earlier work this paper cites.
K. Cho, B. van Merrienboer, D. Bahdanau, and Y. Bengio, “On the Properties of Neural Machine Translation: Encoder-Decoder Approaches”, CoRR
Original
2014
Earlier work this paper cites.

Similar

D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization”, CoRR
Original
2014
Cited alongside, same era.
D. Clevert, T. Unterthiner, and S. Hochreiter, “Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)”, CoRR
Original
2015
Cited alongside, same era.
Software available from tensorflow.org
M. Abadi et al., “TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems”, 2015 · 2015
Cited alongside, same era.
S. Han, H. Mao, and W. J. Dally, “Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding”, CoRR
Original
2015
Cited alongside, same era.
S. Gupta, A. Agrawal, K. Gopalakrishnan, and P. Narayanan, “Deep Learning with Limited Numerical Precision”, CoRR
Original

Then

T. Cheng, “Recursive neural networks in quark/gluon tagging”, Comput. Softw. Big Sci
Original
2018
Later among the works it cites.
S. Macaluso and D. Shih, “Pulling out all the tops with computer vision and deep learning”, JHEP
Original
2018
Later among the works it cites.
K. Datta and A. J. Larkoski, “Novel jet observables from machine learning”, JHEP
Original
2018
Later among the works it cites.
A. Butter, G. Kasieczka, T. Plehn, and M. Russell, “Deep-learned Top Tagging with a Lorentz Layer”, SciPost Phys
Original
2018
Later among the works it cites.
J. Duarte et al., “Fast inference of deep neural networks in FPGAs for particle physics”, JINST
Original

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

2015

Cited alongside, same era.

L. de Oliveira et al., “Jet-images – deep learning edition”, JHEP

2016

Cited alongside, same era.

D. Guest et al., “Jet flavor classification in high-energy physics with deep neural networks”, Phys. Rev. D

2016

Cited alongside, same era.

A. Schwartzman et al., “Image Processing, Computer Vision, and Deep Learning: new approaches to the analysis and physics interpretation of LHC events”, J. Phys. Conf. Ser

2016

Cited alongside, same era.

The GPyOpt authors, “GPyOpt: A Bayesian Optimization framework in python”. http://github.com/SheffieldML/GPyOpt

2016

Cited alongside, same era.

T. Dozat, “Incorporating Nesterov Momentum into Adam”, 2016

2016

Cited alongside, same era.

G. Kasieczka, T. Plehn, M. Russell, and T. Schell, “Deep-learning Top Taggers or The End of QCD?”, JHEP

2017

Cited alongside, same era.

P. T. Komiske, E. M. Metodiev, and M. D. Schwartz, “Deep learning in color: towards automated quark/gluon jet discrimination”, JHEP

2017

Cited alongside, same era.

2018

Later among the works it cites.

12, 2018

N. Choma et al., “Graph Neural Networks for IceCube Signal Classification”, pp. 386–391 · 2018

Later among the works it cites.

E. Coleman et al., “The importance of calorimetry for highly-boosted jet substructure”, JINST

2018

Later among the works it cites.

G. Louppe, K. Cho, C. Becot, and K. Cranmer, “QCD-Aware Recursive Neural Networks for Jet Physics”, JHEP

2019

Closest in time.

P. T. Komiske, E. M. Metodiev, and J. Thaler, “Energy Flow Networks: Deep Sets for Particle Jets”, JHEP

2019

Closest in time.

M. Abdughani, J. Ren, L. Wu, and J. M. Yang, “Probing stop pair production at the LHC with graph neural networks”, Journal of High Energy Physics

2019

Closest in time.

J. Arjona Martínez et al., “Pileup mitigation at the Large Hadron Collider with graph neural networks”, Eur. Phys. J. Plus

2019

Closest in time.

J. Bai, F. Lu, K. Zhang et al., “ONNX: Open Neural Network Exchange”. https://github.com/onnx/onnx

2019

Closest in time.

doi: 10.5281/zenodo.3601436

J. M. Duarte et al., “HLS4ML LHC Jet dataset (30 particles)”, January, 2020 · 2020

Closest in time.

doi: 10.5281/zenodo.3601443

J. M. Duarte et al., “HLS4ML LHC Jet dataset (50 particles)”, January, 2020 · 2020

Closest in time.

doi: 10.5281/zenodo.3602254

J. M. Duarte et al., “HLS4ML LHC Jet dataset (100 particles)”, January, 2020 · 2020

Closest in time.

doi: 10.5281/zenodo.3602260

Duarte, Javier Mauricio and others, “HLS4ML LHC Jet dataset (150 particles)”, January, 2020 · 2020

Closest in time.