Built on

S. Marzani, G. Soyez and M. Spannowsky, Looking inside jets: an introduction to jet substructure and boosted-object phenomenology , 1901.10342
Original
1901
Earlier work this paper cites.
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang and P. S. Yu, A comprehensive survey on graph neural networks , IEEE Transactions on Neural Networks and Learning Systems 32
Original
1901
Earlier work this paper cites.
H. Qu and L. Gouskos, ParticleNet: Jet Tagging via Particle Clouds , Phys. Rev. D 101
Original
1902
Earlier work this paper cites.
T. S. Roy and A. H. Vijay, A robust anomaly finder based on autoencoders , 1903.02032
Original
1903
Earlier work this paper cites.
A. Blance, M. Spannowsky and P. Waite, Adversarially-trained autoencoders for robust unsupervised new physics searches , JHEP 10
Original
1905
Earlier work this paper cites.
1908.02441
Original
J. Park, M. Lee, H. J. Chang, K. Lee and J. Y. Choi, Symmetric graph convolutional autoencoder for unsupervised graph representation learning , in Proceedings of the IEEE/CVF International Conference on Computer Vision , pp. 6519–6528, 2019 · 1908
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M. Wang, D. Zheng, Z. Ye, Q. Gan, M. Li, X. Song et al., Deep graph library: A graph-centric, highly-performant package for graph neural networks , 1909.01315
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1909
Earlier work this paper cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan et al., Pytorch: An imperative style, high-performance deep learning library , 1912.01703
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1912
Earlier work this paper cites.
B. Andersson, G. Gustafson, L. Lonnblad and U. Pettersson, Coherence Effects in Deep Inelastic Scattering , Z. Phys. C43
1989
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S. Catani, Y. L. Dokshitzer, M. Olsson, G. Turnock and B. R. Webber, New clustering algorithm for multi - jet cross-sections in e+ e- annihilation , Phys. Lett. B269
1991
Earlier work this paper cites.
S. D. Ellis and D. E. Soper, Successive combination jet algorithm for hadron collisions , Phys. Rev. D48
1993
Earlier work this paper cites.
Y. Lecun, L. Bottou, Y. Bengio and P. Haffner, Gradient-based learning applied to document recognition , Proceedings of the IEEE 86
1998
Earlier work this paper cites.
B. Nachman and D. Shih, Anomaly Detection with Density Estimation , Phys. Rev. D 101
Original
2001
Earlier work this paper cites.
V. Mikuni and F. Canelli, ABCNet: An attention-based method for particle tagging , Eur. Phys. J. Plus 135
Original
2001
Earlier work this paper cites.
G. Salha, R. Hennequin and M. Vazirgiannis, Simple and effective graph autoencoders with one-hop linear models , 2001.07614
Original
2001
Earlier work this paper cites.

Similar

O. Knapp, O. Cerri, G. Dissertori, T. Q. Nguyen, M. Pierini and J.-R. Vlimant, Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark , Eur. Phys. J. Plus 136
Original
2005
Cited alongside, same era.
T. Sjostrand, S. Mrenna and P. Z. Skands, PYTHIA 6.4 Physics and Manual , JHEP 05
2006
Cited alongside, same era.
A. Lifson, G. P. Salam and G. Soyez, Calculating the primary Lund Jet Plane density , JHEP 10
Original
2007
Cited alongside, same era.
C. K. Khosa and V. Sanz, Anomaly Awareness , 2007.14462
Original
2007
Cited alongside, same era.
T. Cheng, J.-F. Arguin, J. Leissner-Martin, J. Pilette and T. Golling, Variational Autoencoders for Anomalous Jet Tagging , 2007.01850
Original

Then

J. Cogan, M. Kagan, E. Strauss and A. Schwarztman, Jet-Images: Computer Vision Inspired Techniques for Jet Tagging , JHEP 02
Original
2015
Later among the works it cites.
L. de Oliveira, M. Kagan, L. Mackey, B. Nachman and A. Schwartzman, Jet-images — deep learning edition , JHEP 07
Original
2016
Later among the works it cites.
1704.01212
Original
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals and G. E. Dahl, Neural message passing for quantum chemistry , in International Conference on Machine Learning , pp. 1263–1272, PMLR, 2017 · 2017
Later among the works it cites.
J. H. Collins, K. Howe and B. Nachman, Anomaly Detection for Resonant New Physics with Machine Learning , Phys. Rev. Lett. 121
Original
2018
Later among the works it cites.
1802.08352

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2021

Anomaly detection with Convolutional Graph Neural Networks

Atkinson, Oliver, Bhardwaj, Akanksha, Englert, Christoph et al.

Understand

We devise an autoencoder based strategy to facilitate anomaly detection for boosted jets, employing Graph Neural Networks (GNNs) to do so.

To overcome known limitations of GNN autoencoders, we design a symmetric decoder capable of simultaneously reconstructing edge features and node features.
Focusing on latent space based discriminators, we find that such setups provide a promising avenue to isolate new physics and competing SM signatures from sensitivity-limiting QCD jet contributions.
We demonstrate the flexibility and broad applicability of this approach using examples of $W$ bosons, top quarks, and exotic hadronically-decaying exotic scalar bosons.

Built on

S. Marzani, G. Soyez and M. Spannowsky, Looking inside jets: an introduction to jet substructure and boosted-object phenomenology , 1901.10342
Original
1901
Earlier work this paper cites.
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang and P. S. Yu, A comprehensive survey on graph neural networks , IEEE Transactions on Neural Networks and Learning Systems 32
Original
1901
Earlier work this paper cites.
H. Qu and L. Gouskos, ParticleNet: Jet Tagging via Particle Clouds , Phys. Rev. D 101
Original
1902
Earlier work this paper cites.
T. S. Roy and A. H. Vijay, A robust anomaly finder based on autoencoders , 1903.02032
Original
1903
Earlier work this paper cites.
A. Blance, M. Spannowsky and P. Waite, Adversarially-trained autoencoders for robust unsupervised new physics searches , JHEP 10
Original
1905
Earlier work this paper cites.
1908.02441
Original
J. Park, M. Lee, H. J. Chang, K. Lee and J. Y. Choi, Symmetric graph convolutional autoencoder for unsupervised graph representation learning , in Proceedings of the IEEE/CVF International Conference on Computer Vision , pp. 6519–6528, 2019 · 1908
Earlier work this paper cites.
M. Wang, D. Zheng, Z. Ye, Q. Gan, M. Li, X. Song et al., Deep graph library: A graph-centric, highly-performant package for graph neural networks , 1909.01315
Original
1909
Earlier work this paper cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan et al., Pytorch: An imperative style, high-performance deep learning library , 1912.01703
Original
1912
Earlier work this paper cites.
B. Andersson, G. Gustafson, L. Lonnblad and U. Pettersson, Coherence Effects in Deep Inelastic Scattering , Z. Phys. C43
1989
Earlier work this paper cites.
S. Catani, Y. L. Dokshitzer, M. Olsson, G. Turnock and B. R. Webber, New clustering algorithm for multi - jet cross-sections in e+ e- annihilation , Phys. Lett. B269
1991
Earlier work this paper cites.
S. D. Ellis and D. E. Soper, Successive combination jet algorithm for hadron collisions , Phys. Rev. D48
1993
Earlier work this paper cites.
Y. Lecun, L. Bottou, Y. Bengio and P. Haffner, Gradient-based learning applied to document recognition , Proceedings of the IEEE 86
1998
Earlier work this paper cites.
B. Nachman and D. Shih, Anomaly Detection with Density Estimation , Phys. Rev. D 101
Original
2001
Earlier work this paper cites.
V. Mikuni and F. Canelli, ABCNet: An attention-based method for particle tagging , Eur. Phys. J. Plus 135
Original
2001
Earlier work this paper cites.
G. Salha, R. Hennequin and M. Vazirgiannis, Simple and effective graph autoencoders with one-hop linear models , 2001.07614
Original
2001
Earlier work this paper cites.

Similar

O. Knapp, O. Cerri, G. Dissertori, T. Q. Nguyen, M. Pierini and J.-R. Vlimant, Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark , Eur. Phys. J. Plus 136
Original
2005
Cited alongside, same era.
T. Sjostrand, S. Mrenna and P. Z. Skands, PYTHIA 6.4 Physics and Manual , JHEP 05
2006
Cited alongside, same era.
A. Lifson, G. P. Salam and G. Soyez, Calculating the primary Lund Jet Plane density , JHEP 10
Original
2007
Cited alongside, same era.
C. K. Khosa and V. Sanz, Anomaly Awareness , 2007.14462
Original
2007
Cited alongside, same era.
T. Cheng, J.-F. Arguin, J. Leissner-Martin, J. Pilette and T. Golling, Variational Autoencoders for Anomalous Jet Tagging , 2007.01850
Original

Then

J. Cogan, M. Kagan, E. Strauss and A. Schwarztman, Jet-Images: Computer Vision Inspired Techniques for Jet Tagging , JHEP 02
Original
2015
Later among the works it cites.
L. de Oliveira, M. Kagan, L. Mackey, B. Nachman and A. Schwartzman, Jet-images — deep learning edition , JHEP 07
Original
2016
Later among the works it cites.
1704.01212
Original
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals and G. E. Dahl, Neural message passing for quantum chemistry , in International Conference on Machine Learning , pp. 1263–1272, PMLR, 2017 · 2017
Later among the works it cites.
J. H. Collins, K. Howe and B. Nachman, Anomaly Detection for Resonant New Physics with Machine Learning , Phys. Rev. Lett. 121
Original
2018
Later among the works it cites.
1802.08352

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

2007

Cited alongside, same era.

Y. Iiyama et al., Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics , Front. Big Data 3

Original

2008

Cited alongside, same era.

X. Ju and B. Nachman, Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons , Phys. Rev. D102

Original

2008

Cited alongside, same era.

M. Cacciari, G. P. Salam and G. Soyez, The anti- k t k_{t} jet clustering algorithm , JHEP 04

Original

2008

Cited alongside, same era.

B. Nachman, Anomaly Detection for Physics Analysis and Less than Supervised Learning , 2010.14554

Original

2010

Cited alongside, same era.

V. Mikuni and F. Canelli, Unsupervised clustering for collider physics , 2010.07106

Original

2010

Cited alongside, same era.

D. E. Soper and M. Spannowsky, Finding physics signals with shower deconstruction , Phys. Rev. D84

Original

2011

Cited alongside, same era.

F. A. Dreyer and H. Qu, Jet tagging in the Lund plane with graph networks , JHEP 03

Original

2012

Cited alongside, same era.

Original

P. V. Tran, Learning to make predictions on graphs with autoencoders , in 2018 IEEE 5th international conference on data science and advanced analytics (DSAA) , pp. 237–245, IEEE, 2018 · 2018

Later among the works it cites.

I. Brivio and M. Trott, The Standard Model as an Effective Field Theory , Phys. Rept. 793

Original

2019

Later among the works it cites.

A. De Simone and T. Jacques, Guiding New Physics Searches with Unsupervised Learning , Eur. Phys. J. C 79

Original

2019

Later among the works it cites.

M. Abdughani, J. Ren, L. Wu and J. M. Yang, Probing stop pair production at the LHC with graph neural networks , JHEP 08

Original

2019