References

1

Eric A. Moreno, Thong Q. Nguyen, Jean-Roch Vlimant, Olmo Cerri, Harvey B. Newman, Avikar Periwal, Maria Spiropulu, Javier M. Duarte, and Maurizio Pierini. Interaction networks for the identification of boosted $H \rightarrow b\overline b$ decays. Phys. Rev. D, 102:012010, 2020. arXiv:1909.12285, doi:10.1103/PhysRevD.102.012010.

2

Roman Kogler and others. Jet Substructure at the Large Hadron Collider: Experimental Review. Rev. Mod. Phys., 91(4):045003, 2019. arXiv:1803.06991, doi:10.1103/RevModPhys.91.045003.

3

Javier Duarte. Sample with jet, track and secondary vertex properties for Hbb tagging ML studies \texttt HiggsToBBNTuple_HiggsToBB_QCD_RunII_13TeV_MC. 2019. CERN Open Data Portal. URL: http://opendata.cern.ch/record/12102, doi:10.7483/OPENDATA.CMS.JGJX.MS7Q.

4

Jim Pivarski, Peter Elmer, and David Lange. Awkward Arrays in Python, C++, and Numba. In 24th International Conference on Computing in High Energy and Nuclear Physics. 1 2020. arXiv:2001.06307.

5

A.M. Sirunyan and others. Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV. J. Instrum., 13(05):P05011, 2018. arXiv:1712.07158, doi:10.1088/1748-0221/13/05/P05011.

6

Simone Marzani, Gregory Soyez, and Michael Spannowsky. Looking inside jets: an introduction to jet substructure and boosted-object phenomenology. Volume 958. Springer, 2019. arXiv:1901.10342, doi:10.1007/978-3-030-15709-8.

7

Serguei Chatrchyan and others. Identification of b-Quark Jets with the CMS Experiment. J. Instrum., 8:P04013, 2013. arXiv:1211.4462, doi:10.1088/1748-0221/8/04/P04013.

8

CMS Collaboration. Performance of quark/gluon discrimination in 8 TeV pp data. CMS Physics Analysis Summary CMS-PAS-JME-13-002, CERN, 2013. URL: https://cds.cern.ch/record/1599732.

9

Luke de Oliveira, Michael Kagan, Lester Mackey, Benjamin Nachman, and Ariel Schwartzman. Jet-images — deep learning edition. J. High Energy Phys., 07:069, 2016. arXiv:1511.05190, doi:10.1007/JHEP07(2016)069.

10

Markus Stoye, Jan Kieseler, Mauro Verzetti, Huilin Qu, Loukas Gouskos, and Anna Stakia. DeepJet: generic physics object based jet multiclass classification for LHC experiments. In Deep Learning for Physical Sciences Workshop at the 31st Conference on Neural Information Processing Systems (NeurIPS). 2017. URL: https://dl4physicalsciences.github.io/files/nips_dlps_2017_10.pdf.

11

Patrick T. Komiske, Eric M. Metodiev, and Jesse Thaler. Energy Flow Networks: Deep Sets for Particle Jets. J. High Energy Phys., 01:121, 2019. arXiv:1810.05165, doi:10.1007/JHEP01(2019)121.

12

Eric A. Moreno, Olmo Cerri, Javier M. Duarte, Harvey B. Newman, Thong Q. Nguyen, Avikar Periwal, Maurizio Pierini, Aidana Serikova, Maria Spiropulu, and Jean-Roch Vlimant. JEDI-net: a jet identification algorithm based on interaction networks. Eur. Phys. J. C, 80(1):58, 2020. arXiv:1908.05318, doi:10.1140/epjc/s10052-020-7608-4.

13

Huilin Qu and Loukas Gouskos. ParticleNet: Jet Tagging via Particle Clouds. Phys. Rev. D, 101(5):056019, 2020. arXiv:1902.08570, doi:10.1103/PhysRevD.101.056019.

14

Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola. Deep sets. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30, 3391. Curran Associates, Inc., 2017. URL: http://papers.nips.cc/paper/6931-deep-sets.pdf, arXiv:1703.06114.

15

James Dolen, Philip Harris, Simone Marzani, Salvatore Rappoccio, and Nhan Tran. Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure. J. High Energy Phys., 05:156, 2016. arXiv:1603.00027, doi:10.1007/JHEP05(2016)156.

16

ATLAS Collaboration. Performance of mass-decorrelated jet substructure observables for hadronic two-body decay tagging in ATLAS. ATLAS Public Note ATL-PHYS-PUB-2018-014, CERN, 2018. URL: http://cds.cern.ch/record/2630973.

17

Gilles Louppe, Michael Kagan, and Kyle Cranmer. Learning to Pivot with Adversarial Networks. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30, 981. Curran Associates, Inc., 2017. URL: http://papers.nips.cc/paper/6699-learning-to-pivot-with-adversarial-networks.pdf, arXiv:1611.01046.

18

Yaroslav Ganin and Victor Lempitsky. Unsupervised domain adaptation by backpropagation. In Francis Bach and David Blei, editors, Proceedings of the 32nd International Conference on Machine Learning, volume 37 of Proceedings of Machine Learning Research, 1180. Lille, France, 2015. PMLR. URL: http://proceedings.mlr.press/v37/ganin15.html, arXiv:1409.7495.

19

Albert M Sirunyan and others. A deep neural network to search for new long-lived particles decaying to jets. Mach. Learn.: Sci. Technol., 1:035012, 2020. arXiv:1912.12238, doi:10.1088/2632-2153/ab9023.

20

Jonathan Shlomi, Peter Battaglia, and Jean-Roch Vlimant. Graph neural networks in particle physics. Mach. Learn.: Sci. Tech., 2:021001, 7 2020. arXiv:2007.13681, doi:10.1088/2632-2153/abbf9a.

21

Matthias Fey and Jan E. Lenssen. Fast graph representation learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds. 2019. URL: https://pytorch-geometric.readthedocs.io/, arXiv:1903.02428.

22

Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gulcehre, Francis Song, Andrew Ballard, Justin Gilmer, George Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matt Botvinick, Oriol Vinyals, Yujia Li, and Razvan Pascanu. Relational inductive biases, deep learning, and graph networks. Preprint, 2018. arXiv:1806.01261.

23

Sergey Ioffe and Christian Szegedy. Batch normalization: accelerating deep network training by reducing internal covariate shift. In Francis Bach and David Blei, editors, 32nd International Conference on Machine Learning, volume 37, 448. Lille, France, 07 2015. PMLR. URL: http://proceedings.mlr.press/v37/ioffe15.html, arXiv:1502.03167.

24

C.D. Jones and E. Sexton-Kennedy. Stitched Together: Transitioning CMS to a Hierarchical Threaded Framework. J. Phys. Conf. Ser., 513:022034, 2014. doi:10.1088/1742-6596/513/2/022034.

25

Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. Gnnexplainer: generating explanations for graph neural networks. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d\textquotesingle Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019. arXiv:1903.03894.