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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
Original
1902
Earlier work this paper cites.
A. Chakraborty, S. H. Lim, and M. M. Nojiri, Interpretable deep learning for two-prong jet classification with jet spectra
Original
1904
Earlier work this paper cites.
B. M. Dillon, D. A. Faroughy, and J. F. Kamenik, Uncovering latent jet substructure
Original
1904
Earlier work this paper cites.
S. Carrazza and F. A. Dreyer, Lund jet images from generative and cycle-consistent adversarial networks
Original
1909
Earlier work this paper cites.
E. Noether, Invariante variationsprobleme
1918
Earlier work this paper cites.
B. Nachman and D. Shih, Anomaly Detection with Density Estimation
Original
2001
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.
S. Krippendorf and M. Syvaeri, Detecting Symmetries with Neural Networks
Original
2003
Earlier work this paper cites.
B. M. Dillon, D. A. Faroughy, J. F. Kamenik, and M. Szewc, Learning the latent structure of collider events
Original
2005
Earlier work this paper cites.
A. Bogatskiy, B. Anderson, J. T. Offermann, M. Roussi, D. W. Miller, and R. Kondor, Lorentz Group Equivariant Neural Network for Particle Physics
Original
2006
Earlier work this paper cites.
M. Cacciari and G. P. Salam, Dispelling the N 3 N^{3} myth for the k t k_{t} jet-finder
2006
Earlier work this paper cites.
G. Kasieczka, B. Nachman, M. D. Schwartz, and D. Shih, Automating the ABCD method with machine learning
Original
2007
Earlier work this paper cites.

Similar

J. Shlomi, P. Battaglia, and J.-R. Vlimant, Graph Neural Networks in Particle Physics
Original
2007
Cited alongside, same era.
X. Ju and B. Nachman, Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons
Original
2008
Cited alongside, same era.
M. Cacciari, G. P. Salam, and G. Soyez, The anti- k t k_{t} jet clustering algorithm
Original
2008
Cited alongside, same era.
M. Cacciari, G. P. Salam, and G. Soyez, FastJet User Manual
Original
2012
Cited alongside, same era.
F. Wang and H. Liu, Understanding the behaviour of contrastive loss
Original
2012

Then

I. Henrion, K. Cranmer, J. Bruna, K. Cho, J. Brehmer, G. Louppe, and G. Rochette, Neural Message Passing for Jet Physics
2017
Later among the works it cites.
J. Lin, M. Freytsis, I. Moult, and B. Nachman, Boosting H → b b ¯ H\to b\bar{b} with Machine Learning
Original
2018
Later among the works it cites.
S. Macaluso and D. Shih, Pulling Out All the Tops with Computer Vision and Deep Learning
Original
2018
Later among the works it cites.
F. A. Dreyer, G. P. Salam, and G. Soyez, The Lund Jet Plane
Original
2018
Later among the works it cites.
A. Butter, G. Kasieczka, T. Plehn, and M. Russell, Deep-learned Top Tagging with a Lorentz Layer
Original
2018

Beyond the bibliography

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2021

Symmetries, Safety, and Self-Supervision

Dillon, Barry M., Kasieczka, Gregor, Olischlager, Hans et al.

Understand

Collider searches face the challenge of defining a representation of high-dimensional data such that physical symmetries are manifest, the discriminating features are retained, and the choice of representation is new-physics agnostic.

We introduce JetCLR to solve the mapping from low-level data to optimized observables though self-supervised contrastive learning.
As an example, we construct a data representation for top and QCD jets using a permutation-invariant transformer-encoder network and visualize its symmetry properties.
We compare the JetCLR representation with alternative representations using linear classifier tests and find it to work quite well.

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
Original
1902
Earlier work this paper cites.
A. Chakraborty, S. H. Lim, and M. M. Nojiri, Interpretable deep learning for two-prong jet classification with jet spectra
Original
1904
Earlier work this paper cites.
B. M. Dillon, D. A. Faroughy, and J. F. Kamenik, Uncovering latent jet substructure
Original
1904
Earlier work this paper cites.
S. Carrazza and F. A. Dreyer, Lund jet images from generative and cycle-consistent adversarial networks
Original
1909
Earlier work this paper cites.
E. Noether, Invariante variationsprobleme
1918
Earlier work this paper cites.
B. Nachman and D. Shih, Anomaly Detection with Density Estimation
Original
2001
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.
S. Krippendorf and M. Syvaeri, Detecting Symmetries with Neural Networks
Original
2003
Earlier work this paper cites.
B. M. Dillon, D. A. Faroughy, J. F. Kamenik, and M. Szewc, Learning the latent structure of collider events
Original
2005
Earlier work this paper cites.
A. Bogatskiy, B. Anderson, J. T. Offermann, M. Roussi, D. W. Miller, and R. Kondor, Lorentz Group Equivariant Neural Network for Particle Physics
Original
2006
Earlier work this paper cites.
M. Cacciari and G. P. Salam, Dispelling the N 3 N^{3} myth for the k t k_{t} jet-finder
2006
Earlier work this paper cites.
G. Kasieczka, B. Nachman, M. D. Schwartz, and D. Shih, Automating the ABCD method with machine learning
Original
2007
Earlier work this paper cites.

Similar

J. Shlomi, P. Battaglia, and J.-R. Vlimant, Graph Neural Networks in Particle Physics
Original
2007
Cited alongside, same era.
X. Ju and B. Nachman, Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons
Original
2008
Cited alongside, same era.
M. Cacciari, G. P. Salam, and G. Soyez, The anti- k t k_{t} jet clustering algorithm
Original
2008
Cited alongside, same era.
M. Cacciari, G. P. Salam, and G. Soyez, FastJet User Manual
Original
2012
Cited alongside, same era.
F. Wang and H. Liu, Understanding the behaviour of contrastive loss
Original
2012

Then

I. Henrion, K. Cranmer, J. Bruna, K. Cho, J. Brehmer, G. Louppe, and G. Rochette, Neural Message Passing for Jet Physics
2017
Later among the works it cites.
J. Lin, M. Freytsis, I. Moult, and B. Nachman, Boosting H → b b ¯ H\to b\bar{b} with Machine Learning
Original
2018
Later among the works it cites.
S. Macaluso and D. Shih, Pulling Out All the Tops with Computer Vision and Deep Learning
Original
2018
Later among the works it cites.
F. A. Dreyer, G. P. Salam, and G. Soyez, The Lund Jet Plane
Original
2018
Later among the works it cites.
A. Butter, G. Kasieczka, T. Plehn, and M. Russell, Deep-learned Top Tagging with a Lorentz Layer
Original
2018

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

Cited alongside, same era.

DELPHES 3, J. de Favereau, C. Delaere, P. Demin, A. Giammanco, V. Lemaître, A. Mertens, and M. Selvaggi, DELPHES 3, A modular framework for fast simulation of a generic collider experiment

Original

2014

Cited alongside, same era.

J. Cogan, M. Kagan, E. Strauss, and A. Schwarztman, Jet-Images: Computer Vision Inspired Techniques for Jet Tagging

Original

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.

L. de Oliveira, M. Kagan, L. Mackey, B. Nachman, and A. Schwartzman, Jet-images — deep learning edition

Original

2016

Cited alongside, same era.

E. M. Metodiev, B. Nachman, and J. Thaler, Classification without labels: Learning from mixed samples in high energy physics

Original

2017

Cited alongside, same era.

G. Kasieczka, T. Plehn, M. Russell, and T. Schell, Deep-learning Top Taggers or The End of QCD?

Original

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.