Abstract
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.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
I. Brivio and M. Trott, The Standard Model as an Effective Field Theory, Phys. Rept. 793 (2019) 1 [arXiv:1706.08945] [INSPIRE].
J.H. Collins, P. Martín-Ramiro, B. Nachman and D. Shih, Comparing weak- and unsupervised methods for resonant anomaly detection, Eur. Phys. J. C 81 (2021) 617 [arXiv:2104.02092] [INSPIRE].
CMS collaboration, MUSiC: a model-unspecific search for new physics in proton-proton collisions at \( \sqrt{s} \) = 13 TeV, Eur. Phys. J. C 81 (2021) 629 [arXiv:2010.02984] [INSPIRE].
ATLAS collaboration, A strategy for a general search for new phenomena using data-derived signal regions and its application within the ATLAS experiment, Eur. Phys. J. C 79 (2019) 120 [arXiv:1807.07447] [INSPIRE].
J.H. Collins, K. Howe and B. Nachman, Anomaly Detection for Resonant New Physics with Machine Learning, Phys. Rev. Lett. 121 (2018) 241803 [arXiv:1805.02664] [INSPIRE].
A. Blance, M. Spannowsky and P. Waite, Adversarially-trained autoencoders for robust unsupervised new physics searches, JHEP 10 (2019) 047 [arXiv:1905.10384] [INSPIRE].
J. Hajer, Y.-Y. Li, T. Liu and H. Wang, Novelty Detection Meets Collider Physics, Phys. Rev. D 101 (2020) 076015 [arXiv:1807.10261] [INSPIRE].
A. De Simone and T. Jacques, Guiding New Physics Searches with Unsupervised Learning, Eur. Phys. J. C 79 (2019) 289 [arXiv:1807.06038] [INSPIRE].
B. Nachman and D. Shih, Anomaly Detection with Density Estimation, Phys. Rev. D 101 (2020) 075042 [arXiv:2001.04990] [INSPIRE].
B. Nachman, Anomaly Detection for Physics Analysis and Less than Supervised Learning, arXiv:2010.14554 [INSPIRE].
S. Marzani, G. Soyez and M. Spannowsky, Looking inside jets: an introduction to jet substructure and boosted-object phenomenology, vol. 958, Springer (2019), [DOI] [arXiv:1901.10342] [INSPIRE].
D.E. Soper and M. Spannowsky, Finding physics signals with shower deconstruction, Phys. Rev. D 84 (2011) 074002 [arXiv:1102.3480] [INSPIRE].
D.E. Soper and M. Spannowsky, Finding physics signals with event deconstruction, Phys. Rev. D 89 (2014) 094005 [arXiv:1402.1189] [INSPIRE].
ATLAS collaboration, Measurements of the W production cross sections in association with jets with the ATLAS detector, Eur. Phys. J. C 75 (2015) 82 [arXiv:1409.8639] [INSPIRE].
J. Zhou et al., Graph neural networks: A review of methods and applications, (2018) [arXiv:1812.08434].
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang and P.S. Yu, A comprehensive survey on graph neural networks, IEEE Trans. Neural Networks and Learning Systems 32 (2021) 4 [arXiv:1901.00596].
F.A. Dreyer and H. Qu, Jet tagging in the Lund plane with graph networks, JHEP 03 (2021) 052 [arXiv:2012.08526] [INSPIRE].
B. Andersson, G. Gustafson, L. Lönnblad and U. Pettersson, Coherence Effects in Deep Inelastic Scattering, Z. Phys. C 43 (1989) 625 [INSPIRE].
A. Lifson, G.P. Salam and G. Soyez, Calculating the primary Lund Jet Plane density, JHEP 10 (2020) 170 [arXiv:2007.06578] [INSPIRE].
V. Mikuni and F. Canelli, ABCNet: An attention-based method for particle tagging, Eur. Phys. J. Plus 135 (2020) 463 [arXiv:2001.05311] [INSPIRE].
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 (2021) 236 [arXiv:2005.01598] [INSPIRE].
V. Mikuni and F. Canelli, Unsupervised clustering for collider physics, Phys. Rev. D 103 (2021) 092007 [arXiv:2010.07106] [INSPIRE].
G. Dezoort et al., Charged particle tracking via edge-classifying interaction networks, arXiv:2103.16701 [INSPIRE].
M. Abdughani, J. Ren, L. Wu and J.M. Yang, Probing stop pair production at the LHC with graph neural networks, JHEP 08 (2019) 055 [arXiv:1807.09088] [INSPIRE].
Y. Iiyama et al., Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics, Front. Big Data 3 (2020) 598927 [arXiv:2008.03601] [INSPIRE].
M. Farina, Y. Nakai and D. Shih, Searching for New Physics with Deep Autoencoders, Phys. Rev. D 101 (2020) 075021 [arXiv:1808.08992] [INSPIRE].
T. Heimel, G. Kasieczka, T. Plehn and J.M. Thompson, QCD or What?, SciPost Phys. 6 (2019) 030 [arXiv:1808.08979] [INSPIRE].
T.S. Roy and A.H. Vijay, A robust anomaly finder based on autoencoders, arXiv:1903.02032 [INSPIRE].
C.K. Khosa and V. Sanz, Anomaly Awareness, arXiv:2007.14462 [INSPIRE].
T. Finke, M. Krämer, A. Morandini, A. Mück and I. Oleksiyuk, Autoencoders for unsupervised anomaly detection in high energy physics, JHEP 06 (2021) 161 [arXiv:2104.09051] [INSPIRE].
T. Cheng, J.-F. Arguin, J. Leissner-Martin, J. Pilette and T. Golling, Variational Autoencoders for Anomalous Jet Tagging, arXiv:2007.01850 [INSPIRE].
J. Cogan, M. Kagan, E. Strauss and A. Schwarztman, Jet-Images: Computer Vision Inspired Techniques for Jet Tagging, JHEP 02 (2015) 118 [arXiv:1407.5675] [INSPIRE].
L. de Oliveira, M. Kagan, L. Mackey, B. Nachman and A. Schwartzman, Jet-images — deep learning edition, JHEP 07 (2016) 069 [arXiv:1511.05190] [INSPIRE].
X. Ju and B. Nachman, Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons, Phys. Rev. D 102 (2020) 075014 [arXiv:2008.06064] [INSPIRE].
H. Qu and L. Gouskos, ParticleNet: Jet Tagging via Particle Clouds, Phys. Rev. D 101 (2020) 056019 [arXiv:1902.08570] [INSPIRE].
A. Blance and M. Spannowsky, Unsupervised Event Classification with Graphs on Classical and Photonic Quantum Computers, arXiv:2103.03897 [INSPIRE].
G. Kasieczka et al., The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics, arXiv:2101.08320 [INSPIRE].
T.N. Kipf and M. Welling, Variational graph auto-encoders, (2016) [arXiv:1611.07308].
P.V. Tran, Learning to make predictions on graphs with autoencoders, in 2018 IEEE 5th international conference on data science and advanced analytics (DSAA), IEEE, (2018) [arXiv:1802.08352].
G. Salha, R. Hennequin and M. Vazirgiannis, Simple and effective graph autoencoders with one-hop linear models, (2020) [arXiv:2001.07614].
S. Pan, R. Hu, G. Long, J. Jiang, L. Yao and C. Zhang, Adversarially regularized graph autoencoder for graph embedding, (2018) [arXiv:1802.04407].
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, (2019) [arXiv:1908.02441].
J. Alwall et al., The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations, JHEP 07 (2014) 079 [arXiv:1405.0301] [INSPIRE].
T. Sjöstrand, S. Mrenna and P.Z. Skands, PYTHIA 6.4 Physics and Manual, JHEP 05 (2006) 026 [hep-ph/0603175] [INSPIRE].
M. Cacciari, G.P. Salam and G. Soyez, The anti-kt jet clustering algorithm, JHEP 04 (2008) 063 [arXiv:0802.1189] [INSPIRE].
M. Cacciari, G.P. Salam and G. Soyez, FastJet User Manual, Eur. Phys. J. C 72 (2012) 1896 [arXiv:1111.6097] [INSPIRE].
Performance of shower deconstruction in ATLAS, CERN, Geneva (Feb, 2014) ATLAS-CONF-2014-003.
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. B 269 (1991) 432 [INSPIRE].
S.D. Ellis and D.E. Soper, Successive combination jet algorithm for hadron collisions, Phys. Rev. D 48 (1993) 3160 [hep-ph/9305266] [INSPIRE].
M. Wang et al., Deep graph library: A graph-centric, highly-performant package for graph neural networks, (2019) [arXiv:1909.01315].
A. Paszke et al., Pytorch: An imperative style, high-performance deep learning library, (2019) [arXiv:1912.01703].
Y. Lecun, L. Bottou, Y. Bengio and P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE 86 (1998) 2278.
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, PMLR, (2017) [arXiv:1704.01212].
Y. Wang, Y. Sun, Z. Liu, S.E. Sarma, M.M. Bronstein and J.M. Solomon, Dynamic Graph CNN for Learning on Point Clouds, Acm Transactions On Graphics (tog) 38 (2019) 1 arXiv:1801.07829 [INSPIRE].
D.P. Kingma and J. Ba, Adam: A Method for Stochastic Optimization, arXiv:1412.6980 [INSPIRE].
B.M. Dillon, D.A. Faroughy, J.F. Kamenik and M. Szewc, Learning the latent structure of collider events, JHEP 10 (2020) 206 [arXiv:2005.12319] [INSPIRE].
B. Bortolato, B.M. Dillon, J.F. Kamenik and A. Smolkovič, Bump Hunting in Latent Space, arXiv:2103.06595 [INSPIRE].
B.M. Dillon, T. Plehn, C. Sauer and P. Sorrenson, Better Latent Spaces for Better Autoencoders, arXiv:2104.08291 [INSPIRE].
D.P. Kingma and M. Welling, Auto-Encoding Variational Bayes, arXiv:1312.6114 [INSPIRE].
A. Makhzani, J. Shlens, N. Jaitly, I. Goodfellow and B. Frey, Adversarial autoencoders, (2015) [arXiv:1511.05644].
G. Patrini et al., Sinkhorn autoencoders, (2018) [arXiv:1810.01118].
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
ArXiv ePrint: 2105.07988
Rights and permissions
Open Access . This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.
About this article
Cite this article
Atkinson, O., Bhardwaj, A., Englert, C. et al. Anomaly detection with convolutional Graph Neural Networks. J. High Energ. Phys. 2021, 80 (2021). https://doi.org/10.1007/JHEP08(2021)080
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/JHEP08(2021)080