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S. R. Qasim, J. Kieseler, Y. Iiyama, and M. Pierini, Learning representations of irregular particle-detector geometry with distance-weighted graph networks
Original
1902
Earlier work this paper cites.
A. Butter et al., The Machine Learning Landscape of Top Taggers
Original
1902
Earlier work this paper cites.
C. Chen, L. Z. Fragonara, and A. Tsourdos, GAPNet: Graph Attention based Point Neural Network for Exploiting Local Feature of Point Cloud
Original
1905
Earlier work this paper cites.
E. A. Moreno, O. Cerri, J. M. Duarte, H. B. Newman, T. Q. Nguyen, A. Periwal, M. Pierini, A. Serikova, M. Spiropulu, and J.-R. Vlimant, JEDI-net: a jet identification algorithm based on interaction networks
Original
1908
Earlier work this paper cites.
V. Mikuni and F. Canelli, ABCNet: An attention-based method for particle tagging
Original
2001
Earlier work this paper cites.
arXiv:2003.11603
Original
X. Ju et al., Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors · 2003
Earlier work this paper cites.
J. Shlomi, P. Battaglia, and J.-R. Vlimant, Graph Neural Networks in Particle Physics
Original
2007
Earlier work this paper cites.
J. Shlomi, S. Ganguly, E. Gross, K. Cranmer, Y. Lipman, H. Serviansky, H. Maron, and N. Segol, Secondary Vertex Finding in Jets with Neural Networks
Original
2008
Earlier work this paper cites.
M. Cacciari, G. P. Salam, and G. Soyez, The anti- k T k_{T} jet clustering algorithm
Original
2008
Earlier work this paper cites.
M. J. Fenton, A. Shmakov, T.-W. Ho, S.-C. Hsu, D. Whiteson, and P. Baldi, Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks
Original
2010
Earlier work this paper cites.
J. Thaler and K. Van Tilburg, Identifying Boosted Objects with N-subjettiness
Original
2011
Earlier work this paper cites.
M. J. Dolan and A. Ore, Equivariant Energy Flow Networks for Jet Tagging
Original
2012
Earlier work this paper cites.

Similar

J. Duarte and J.-R. Vlimant, Graph Neural Networks for Particle Tracking and Reconstruction
Original
2012
Cited alongside, same era.
J. S. H. Lee, I. Park, I. J. Watson, and S. Yang, Zero-Permutation Jet-Parton Assignment using a Self-Attention Network
Original
2012
Cited alongside, same era.
J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, Empirical evaluation of gated recurrent neural networks on sequence modeling
2014
Cited alongside, same era.
D. P. Kingma and J. Ba, Adam: A Method for Stochastic Optimization
Original
2014
Cited alongside, same era.
DELPHES 3
Original
2014
Cited alongside, same era.

Then

J. Duarte et al., Fast inference of deep neural networks in FPGAs for particle physics
Original
2018
Later among the works it cites.
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, Graph attention networks
2018
Later among the works it cites.
Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon, Dynamic graph cnn for learning on point clouds
2019
Later among the works it cites.
P. T. Komiske, E. M. Metodiev, and J. Thaler, Energy Flow Networks: Deep Sets for Particle Jets
Original
2019
Later among the works it cites.
G. Kasieczka, T. Plehn, J. Thompson, and M. Russel, Top quark tagging reference dataset
2019
Later among the works it cites.

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Reading the bibliography…

2021

Point Cloud Transformers applied to Collider Physics

Mikuni, Vinicius, Canelli, Florencia

Understand

Methods for processing point cloud information have seen a great success in collider physics applications.

One recent breakthrough in machine learning is the usage of Transformer networks to learn semantic relationships between sequences in language processing.
In this work, we apply a modified Transformer network called Point Cloud Transformer as a method to incorporate the advantages of the Transformer architecture to an unordered set of particles resulting from collision events.
To compare the performance with other strategies, we study jet-tagging applications for highly-boosted particles.

Built on

S. R. Qasim, J. Kieseler, Y. Iiyama, and M. Pierini, Learning representations of irregular particle-detector geometry with distance-weighted graph networks
Original
1902
Earlier work this paper cites.
A. Butter et al., The Machine Learning Landscape of Top Taggers
Original
1902
Earlier work this paper cites.
C. Chen, L. Z. Fragonara, and A. Tsourdos, GAPNet: Graph Attention based Point Neural Network for Exploiting Local Feature of Point Cloud
Original
1905
Earlier work this paper cites.
E. A. Moreno, O. Cerri, J. M. Duarte, H. B. Newman, T. Q. Nguyen, A. Periwal, M. Pierini, A. Serikova, M. Spiropulu, and J.-R. Vlimant, JEDI-net: a jet identification algorithm based on interaction networks
Original
1908
Earlier work this paper cites.
V. Mikuni and F. Canelli, ABCNet: An attention-based method for particle tagging
Original
2001
Earlier work this paper cites.
arXiv:2003.11603
Original
X. Ju et al., Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors · 2003
Earlier work this paper cites.
J. Shlomi, P. Battaglia, and J.-R. Vlimant, Graph Neural Networks in Particle Physics
Original
2007
Earlier work this paper cites.
J. Shlomi, S. Ganguly, E. Gross, K. Cranmer, Y. Lipman, H. Serviansky, H. Maron, and N. Segol, Secondary Vertex Finding in Jets with Neural Networks
Original
2008
Earlier work this paper cites.
M. Cacciari, G. P. Salam, and G. Soyez, The anti- k T k_{T} jet clustering algorithm
Original
2008
Earlier work this paper cites.
M. J. Fenton, A. Shmakov, T.-W. Ho, S.-C. Hsu, D. Whiteson, and P. Baldi, Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks
Original
2010
Earlier work this paper cites.
J. Thaler and K. Van Tilburg, Identifying Boosted Objects with N-subjettiness
Original
2011
Earlier work this paper cites.
M. J. Dolan and A. Ore, Equivariant Energy Flow Networks for Jet Tagging
Original
2012
Earlier work this paper cites.

Similar

J. Duarte and J.-R. Vlimant, Graph Neural Networks for Particle Tracking and Reconstruction
Original
2012
Cited alongside, same era.
J. S. H. Lee, I. Park, I. J. Watson, and S. Yang, Zero-Permutation Jet-Parton Assignment using a Self-Attention Network
Original
2012
Cited alongside, same era.
J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, Empirical evaluation of gated recurrent neural networks on sequence modeling
2014
Cited alongside, same era.
D. P. Kingma and J. Ba, Adam: A Method for Stochastic Optimization
Original
2014
Cited alongside, same era.
DELPHES 3
Original
2014
Cited alongside, same era.

Then

J. Duarte et al., Fast inference of deep neural networks in FPGAs for particle physics
Original
2018
Later among the works it cites.
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, Graph attention networks
2018
Later among the works it cites.
Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon, Dynamic graph cnn for learning on point clouds
2019
Later among the works it cites.
P. T. Komiske, E. M. Metodiev, and J. Thaler, Energy Flow Networks: Deep Sets for Particle Jets
Original
2019
Later among the works it cites.
G. Kasieczka, T. Plehn, J. Thompson, and M. Russel, Top quark tagging reference dataset
2019
Later among the works it cites.

Beyond the bibliography

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

Open on alphaXiv

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Software available from tensorflow.org

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, TensorFlow: Large-scale machine learning on heterogeneous systems · 2015

Cited alongside, same era.

T. Sjöstrand, S. Ask, J. R. Christiansen, R. Corke, N. Desai, P. Ilten, S. Mrenna, S. Prestel, C. O. Rasmussen, and P. Z. Skands, An Introduction to PYTHIA 8.2

Original

2015

Cited alongside, same era.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. u. Kaiser, and I. Polosukhin, Attention is all you need

2017

Cited alongside, same era.

P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, Graph attention networks

2017

Cited alongside, same era.

P. T. Komiske, E. M. Metodiev, and M. D. Schwartz, Deep learning in color: towards automated quark/gluon jet discrimination

Original

2017

Cited alongside, same era.

E. Coleman, M. Freytsis, A. Hinzmann, M. Narain, J. Thaler, N. Tran, and C. Vernieri, The importance of calorimetry for highly-boosted jet substructure

Original

2018

Cited alongside, same era.

J. Pata, J. Duarte, J.-R. Vlimant, M. Pierini, and M. Spiropulu, MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks

Original

Cited in the paper.

P. T. Komiske, E. M. Metodiev, and J. Thaler, Energy flow networks: deep sets for particle jets

2019