B.H. Denby, Neural networks and cellular automata in experimental high-energy physics. Comput. Phys. Commun. 49, 429–448 (1988)
Article
ADS
Google Scholar
C. Peterson, Track finding with neural networks. Nucl. Instrum. Methods A 279, 537–545 (1989)
Article
ADS
Google Scholar
P. Abreu et al., Classification of the hadronic decays of the Z0 into b and c quark pairs using a neural network. Phys. Lett. B 295, 383–395 (1992)
Article
ADS
Google Scholar
B.H. Denby, Neural networks in high-energy physics: a ten year perspective. Comput. Phys. Commun. 119, 219–231 (1999)
Article
ADS
Google Scholar
H.-J. Yang, B.P. Roe, J. Zhu, Studies of boosted decision trees for MiniBooNE particle identification. Nucl. Instrum. Methods A 555, 370–385 (2005)
Article
ADS
Google Scholar
A. Radovic et al., Machine learning at the energy and intensity frontiers of particle physics. Nature 560(7716), 41 (2018)
Article
ADS
Google Scholar
V. Khachatryan et al., CMS Phase 1 heavy flavour identification performance and developments, Technical report CMS-DP-2017-013 (2017)
V. Khachatryan et al., New developments for jet substructure reconstruction in CMS. Technical report CMS-DP-2017-027 (2017)
A.A. Pol et al., Detector monitoring with artificial neural networks at the CMS experiment at the CERN large hadron collider. Comput. Softw. Big Sci. 3, 3 (2019)
Article
Google Scholar
A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017). https://doi.org/10.1145/3065386
Article
Google Scholar
CMS Collaboration, The phase-2 upgrade of the CMS Endcap calorimeter. Technical report CERN-LHCC-2017-023. CMS-TDR-019 (2017)
V. Khachatryan et al., Technical proposal for the phase-II upgrade of the CMS detector. Technical report CERN-LHCC-2015-010. LHCC-P-008. CMS-TDR-15-02 (2015)
F. Carminati et al., Calorimetry with deep learning: particle classification, energy regression, and simulation for high-energy physics. Deep Learning for Physical Sciences workshop at NIPS 2017 (2017). https://dl4physicalsciences.github.io/files/nips_dlps_2017_15.pdf
D. Guest, K. Cranmer, D. Whiteson, Deep learning and its application to LHC physics. Ann. Rev. Nucl. Part. Sci. 68, 161–181 (2018)
Article
ADS
Google Scholar
L. De Oliveira, B. Nachman, M. Paganini, Electromagnetic showers beyond shower shapes. (2018). arXiv:1806.05667 [hep-ex]
A. Abada et al., FCC-hh: The Hadron Collider. Eur. Phys. J. Spec. Topics 228(4), 755–1107 (2019). https://doi.org/10.1140/epjst/e2019-900087-0
Article
ADS
Google Scholar
J. Cogan et al., Jet-images: computer vision inspired techniques for jet tagging. JHEP 02, 118 (2015)
Article
ADS
Google Scholar
P.T. Komiske, E.M. Metodiev, M.D. Schwartz, Deep learning in color: towards automated quark/gluon jet discrimination. JHEP 01, 110 (2017)
Article
ADS
Google Scholar
L. de Oliveira et al., Jet-images—deep learning edition. JHEP 07, 069 (2016)
Article
Google Scholar
P. Baldi et al., Jet substructure classification in high-energy physics with deep neural networks. Phys. Rev. D 93(9), 094034 (2016)
Article
ADS
Google Scholar
L. de Oliveira, M. Paganini, B. Nachman, Learning particle physics by example: location-aware generative adversarial networks for physics synthesis. Comput. Softw. Big Sci. 1(1), 4 (2017)
Article
Google Scholar
M. Paganini, L. de Oliveira, B. Nachman, CaloGAN: simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks. Phys. Rev. D 97(1), 014021 (2018)
Article
ADS
Google Scholar
G. Rukhkhattak, S. Vallecorsa, F. Carminati, Three dimensional energy parametrized generative adversarial networks for electromagnetic shower simulation. in 2018 25th IEEE International Conference on Image Processing (ICIP) (2018), pp. 3913–3917
P. Musella, F. Pandolfi, Fast and accurate simulation of particle detectors using generative adversarial networks. Comput. Softw. Big Sci. 2(1), 8 (2018)
Article
Google Scholar
P.T. Komiske et al., Pileup mitigation with machine learning (PUMML). JHEP 12, 51 (2017)
Article
ADS
Google Scholar
ATLAS Collaboration, Identification of jets containing \(b\)-hadrons with recurrent neural networks at the ATLAS experiment. Technical report ATL-PHYS-PUB-2017-003 (2017)
G. Louppe et al., QCD-aware recursive neural networks for jet physics. JHEP 01, 57 (2019)
Article
ADS
Google Scholar
H. Qu, L. Gouskos, ParticleNet: jet tagging via particle clouds. (2019). arXiv:1902.08570 [hep-ph]
P.T. Komiske, E.M. Metodiev, J. Thaler, Energy flow networks: deep sets for particle jets. JHEP 01, 121 (2019)
Article
ADS
Google Scholar
T.Q. Nguyen et al., Topology classification with deep learning to improve real-time event selection at the LHC. (2018). arXiv:1807.00083 [hep-ex]
A.M. Sirunyan et al., Particle-flow reconstruction and global event description with the CMS detector. JINST 12(10), P10003 (2017)
Article
Google Scholar
M. Aaboud et al., Jet reconstruction and performance using particle flow with the ATLAS detector. Eur. Phys. J. C 77(7), 466 (2017)
Article
ADS
Google Scholar
F. Scarselli et al., The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61–80 (2009)
Article
Google Scholar
P.W. Battaglia et al., Relational inductive biases, deep learning, and graph networks. (2018). arXiv:1806.01261
M. Defferrard, X. Bresson, P. Vandergheynst, Convolutional neural networks on graphs with fast localized spectral filtering. in Advances in Neural Information Processing Systems 29, eds. by D.D. Lee, M. Sugiyama, U.V. Luxburg, I. Guyon, R. Garnett (Curran Associates, Inc., 2016), pp. 3844–3852. http://papers.nips.cc/paper/6081-convolutional-neural-networks-ongraphs-with-fast-localized-spectral-filtering.pdf
P. Velickovic et al., Graph attention networks. in International Conference on Learning Representations (2018). https://openreview.net/forum?id=rJXMpikCZ
C. Selvi, E. Sivasankar, A novel adaptive genetic neural network (AGNN) model for recommender systems using modified k-means clustering approach. Multimed. Tools Appl. 78(11), 14303–14330 (2019). https://doi.org/10.1007/s11042-018-6790-y
Article
Google Scholar
I. Henrion et al., Neural message passing for jet physics. Deep Learning for Physical Sciences workshop at NIPS 2017 (2017). https://cims.nyu.edu/~bruna/Media/nmp_jet.pdf
M. Abdughani et al., Probing stop with graph neural network at the LHC. (2018). arXiv:1807.09088 [hep-ph]
J. Arjona Martinez, O. Cerri, M. Pierini, M. Spiropulu, J.-R. Vlimant, Pileup mitigation at the large hadron collider with graph neural networks. Eur. Phys. J. Plus 134, 333 (2018). https://doi.org/10.1140/epjp/i2019-12710-3. arXiv:1807.07988 [hep-ph]
J. Gilmer et al., Neural message passing for quantum chemistry in Proceedings of the 34th International Conference on Machine Learning - Volume 70. 1263–1272 (2017)
Y. Wang et al., Dynamic graph cnn for learning on point clouds. (2018). arXiv:1801.07829 [cs.CV]
M. Abadi et al., TensorFlow: large-scale machine learning on heterogeneous systems, 2015. Software available from http://www.tensorflow.org
S. Agostinelli et al., GEANT4: a simulation toolkit. Nucl. Instrum. Methods A 506, 250–303 (2003)
Article
ADS
Google Scholar
S. Ioffe, C. Szegedy, Batch normalization: accelerating deep network training by reducing internal covariate shift. in Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6–11 July 2015, pp. 448–456 (2015)
D.P. Kingma, J. Ba, Adam: a method for stochastic optimization. in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015 (2015). arXiv:1412.6980
L.N. Smith, N. Topin, Super-convergence: very fast training of residual networks using large learning rates. (2017). arXiv:1708.07120