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Jet tagging is a critical yet challenging classification task in particle physics.
The ATLAS Experiment at the CERN Large Hadron Collider
ATLAS Collaboration · 2008
Earlier work this paper cites.
The anti- k t k_{t} jet clustering algorithm
Cacciari, M., Salam, G. P., and Soyez, G · 2008
Earlier work this paper cites.
The CMS Experiment at the CERN LHC
CMS Collaboration · 2008
Earlier work this paper cites.
Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC
ATLAS Collaboration · 2012
Earlier work this paper cites.
FastJet User Manual
Cacciari, M., Salam, G. P., and Soyez, G · 2012
Earlier work this paper cites.
Observation of a New Boson at a Mass of 125 GeV with the CMS Experiment at the LHC
CMS Collaboration · 2012
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The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
Alwall, J., Frederix, R., Frixione, S., Hirschi, V., Maltoni, F., Mattelaer, O., Shao, H. S., Stelzer, T., Torrielli, P., and Zaro, M · 2014
Earlier work this paper cites.
Description and performance of track and primary-vertex reconstruction with the CMS tracker
CMS Collaboration · 2014
Earlier work this paper cites.
DELPHES 3, A modular framework for fast simulation of a generic collider experiment
de Favereau, J., Delaere, C., Demin, P., Giammanco, A., Lemaître, V., Mertens, A., and Selvaggi, M · 2014
Earlier work this paper cites.
An introduction to PYTHIA 8.2
Sjöstrand, T., Ask, S., Christiansen, J. R., Corke, R., Desai, N., Ilten, P., Mrenna, S., Prestel, S., Rasmussen, C. O., and Skands, P. Z · 2015
Earlier work this paper cites.
Jet-images — deep learning edition
de Oliveira, L., Kagan, M., Mackey, L., Nachman, B., and Schwartzman, A · 2016
Earlier work this paper cites.
Jet Flavor Classification in High-Energy Physics with Deep Neural Networks
Guest, D., Collado, J., Baldi, P., Hsu, S.-C., Urban, G., and Whiteson, D · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Neural Message Passing for Jet Physics
Henrion, I., Brehmer, J., Bruna, J., Cho, K., Cranmer, K., Louppe, G., and Rochette, G · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. u., and Polosukhin, I · 2017
Earlier work this paper cites.
Deep sets
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R. R., and Smola, A. J · 2017
Earlier work this paper cites.
The Lund Jet Plane
Dreyer, F. A., Salam, G. P., and Soyez, G · 2018
Earlier work this paper cites.
Machine learning at the energy and intensity frontiers of particle physics
Radovic, A., Williams, M., Rousseau, D., Kagan, M., Bonacorsi, D., Himmel, A., Aurisano, A., Terao, K., and Wongjirad, T · 2018
Earlier work this paper cites.
The Machine Learning landscape of top taggers
Butter, A. et al · 2019
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
Cited alongside, same era.
Sample with jet, track and secondary vertex properties for Hbb tagging ML studies HiggsToBBNTuple_HiggsToBB_QCD_RunII_13TeV_MC, 2019
Duarte, J · 2019
Cited alongside, same era.
Top Quark Tagging Reference Dataset, March 2019
Kasieczka, G., Plehn, T., Thompson, J., and Russel, M · 2019
Cited alongside, same era.
Jet Substructure at the Large Hadron Collider: Experimental Review
Kogler, R. et al · 2019
Cited alongside, same era.
Energy Flow Networks: Deep Sets for Particle Jets
Komiske, P. T., Metodiev, E. M., and Thaler, J · 2019
Gapointnet: Graph attention based point neural network for exploiting local feature of point cloud
Chen, C., Fragonara, L. Z., and Tsourdos, A · 2021
Later among the works it cites.
Search for top squark production in fully-hadronic final states in proton-proton collisions at s = \sqrt{s}= 13 TeV
CMS Collaboration · 2021
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Symmetries, Safety, and Self-Supervision
Dillon, B. M., Kasieczka, G., Olischlager, H., Plehn, T., Sorrenson, P., and Vogel, L · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2021
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Quarks and gluons in the Lund plane
Dreyer, F., Soyez, G., and Takacs, A · 2021
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning
Larkoski, A. J., Moult, I., and Nachman, B · 2019
Cited alongside, same era.
QCD-Aware Recursive Neural Networks for Jet Physics
Louppe, G., Cho, K., Becot, C., and Cranmer, K · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
Cited alongside, same era.
Dynamic graph cnn for learning on point clouds
Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., and Solomon, J. M · 2019
Cited alongside, same era.
Lookahead optimizer: k steps forward, 1 step back
Zhang, M., Lucas, J., Ba, J., and Hinton, G. E · 2019
Cited alongside, same era.
Lorentz group equivariant neural network for particle physics
Bogatskiy, A., Anderson, B., Offermann, J., Roussi, M., Miller, D., and Kondor, R · 2020
Cited alongside, same era.
Jet tagging in the Lund plane with graph networks
Dreyer, F. A. and Qu, H · 2021
Later among the works it cites.
PCT: Point cloud transformer
Guo, M.-H., Cai, J.-X., Liu, Z.-N., Mu, T.-J., Martin, R. R., and Hu, S.-M · 2021
Later among the works it cites.
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., Back, T., Petersen, S., Reiman, D., Clancy, E., Zielinski, M., Steinegger, M., Pacholska, M., Berghammer, T., Bodenstein, S., Silver, D., Vinyals, O., Senior, A. W., Kavukcuoglu, K., Kohli, P., and Hassabis, D · 2021
Later among the works it cites.
Swin transformer: Hierarchical vision transformer using shifted windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B · 2021
Later among the works it cites.
Point cloud transformers applied to collider physics
Mikuni, V. and Canelli, F · 2021
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Particle Convolution for High Energy Physics
Shimmin, C · 2021
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Normformer: Improved transformer pretraining with extra normalization
Shleifer, S., Weston, J., and Ott, M · 2021
Later among the works it cites.
Going deeper with image transformers
Touvron, H., Cord, M., Sablayrolles, A., Synnaeve, G., and Jégou, H · 2021
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Informer: Beyond efficient transformer for long sequence time-series forecasting
Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., and Zhang, W · 2021
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A FAIR and AI-ready Higgs boson decay dataset
Chen, Y., Huerta, E. A., Duarte, J., Harris, P., Katz, D. S., Neubauer, M. S., Diaz, D., Mokhtar, F., Kansal, R., Park, S. E., Kindratenko, V. V., Zhao, Z., and Rusack, R · 2022
Closest in time.
Leveraging universality of jet taggers through transfer learning
Dreyer, F. A., Grabarczyk, R., and Monni, P. F · 2022
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An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging
Gong, S., Meng, Q., Zhang, J., Qu, H., Li, C., Qian, S., Du, W., Ma, Z.-M., and Liu, T.-Y · 2022
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JetClass: A large-scale dataset for deep learning in jet physics, June 2022
Qu, H., Li, C., and Qian, S · 2022
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