Built on

Y.L. Dokshitzer, G. Leder, S. Moretti and B. Webber, Better jet clustering algorithms , Journal of High Energy Physics 1997
1997
Earlier work this paper cites.
M. Cacciari, G.P. Salam and G. Soyez, The anti- k t k_{t} jet clustering algorithm , JHEP 04
Original
2008
Earlier work this paper cites.
J. Casalderrey-Solana, J.G. Milhano, D. Pablos, K. Rajagopal and X. Yao, Jet Wake from Linearized Hydrodynamics , JHEP 05
Original
2010
Earlier work this paper cites.
J. Bergstra, R. Bardenet, Y. Bengio and B. Kégl, Algorithms for hyper-parameter optimization , in 25th annual conference on neural information processing systems (NIPS 2011) , vol. 24, Neural Information Processing Systems Foundation, 2011
2011
Earlier work this paper cites.
L. Apolinário, A. Cordeiro and K. Zapp, Time reclustering for jet quenching studies , 2012.02199
Original
2012
Earlier work this paper cites.
C. Young, B. Schenke, S. Jeon and C. Gale, MARTINI event generator for heavy quarks: Initialization, parton evolution, and hadronization , Phys. Rev. C 86
Original
2012
Earlier work this paper cites.
M. Cacciari, G.P. Salam and G. Soyez, FastJet User Manual , Eur. Phys. J. C 72
Original
2012
Earlier work this paper cites.
J. Casalderrey-Solana, D.C. Gulhan, J.G. Milhano, D. Pablos and K. Rajagopal, A Hybrid Strong/Weak Coupling Approach to Jet Quenching , JHEP 10
Original
2014
Earlier work this paper cites.

Similar

K.C. Zapp, JEWEL 2.0.0: directions for use , Eur. Phys. J. C 74
Original
2014
Cited alongside, same era.
A.J. Larkoski, S. Marzani, G. Soyez and J. Thaler, Soft Drop , JHEP 05
Original
2014
Cited alongside, same era.
D.P. Kingma and J. Ba, Adam: A method for stochastic optimization , arXiv preprint arXiv:1412.6980 (2014)
Original
2014
Cited alongside, same era.
M. Abadi et al., TensorFlow: Large-scale machine learning on heterogeneous systems , 2015
2015
Cited alongside, same era.
F. Chollet et al., “Keras.” https://keras.io , 2015
2015
Cited alongside, same era.

Then

M. Sundararajan, A. Taly and Q. Yan, Axiomatic attribution for deep networks , in International Conference on Machine Learning , pp. 3319–3328, PMLR, 2017
2017
Later among the works it cites.
L. Apolinário, J.G. Milhano, M. Ploskon and X. Zhang, Novel subjet observables for jet quenching in heavy-ion collisions , Eur. Phys. J. C 78
Original
2018
Later among the works it cites.
G. Milhano, U.A. Wiedemann and K.C. Zapp, Sensitivity of jet substructure to jet-induced medium response , Phys. Lett. B 779
Original
2018
Later among the works it cites.
J. Brewer, J.G. Milhano and J. Thaler, Sorting out quenched jets , Phys. Rev. Lett. 122
Original
2019
Later among the works it cites.

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2021

Deep Learning for the Classification of Quenched Jets

Apolinário, L., Castro, N. F., Romão, M. Crispim et al.

Understand

An important aspect of the study of Quark-Gluon Plasma (QGP) in ultra-relativistic collisions of heavy ions is the ability to identify, in experimental data, a subset of the jets that were strongly modified by the interaction with the QGP.

In this work, we propose studying deep learning techniques for this purpose.
Samples of $Z+$jet events were simulated in vacuum and medium and used to train deep neural networks with the objective of discriminating between medium- and vacuum-like jets.
Dedicated Convolutional Neural Networks, Dense Neural Networks and Recurrent Neural Networks were developed and trained, and their performance was studied.

Built on

Y.L. Dokshitzer, G. Leder, S. Moretti and B. Webber, Better jet clustering algorithms , Journal of High Energy Physics 1997
1997
Earlier work this paper cites.
M. Cacciari, G.P. Salam and G. Soyez, The anti- k t k_{t} jet clustering algorithm , JHEP 04
Original
2008
Earlier work this paper cites.
J. Casalderrey-Solana, J.G. Milhano, D. Pablos, K. Rajagopal and X. Yao, Jet Wake from Linearized Hydrodynamics , JHEP 05
Original
2010
Earlier work this paper cites.
J. Bergstra, R. Bardenet, Y. Bengio and B. Kégl, Algorithms for hyper-parameter optimization , in 25th annual conference on neural information processing systems (NIPS 2011) , vol. 24, Neural Information Processing Systems Foundation, 2011
2011
Earlier work this paper cites.
L. Apolinário, A. Cordeiro and K. Zapp, Time reclustering for jet quenching studies , 2012.02199
Original
2012
Earlier work this paper cites.
C. Young, B. Schenke, S. Jeon and C. Gale, MARTINI event generator for heavy quarks: Initialization, parton evolution, and hadronization , Phys. Rev. C 86
Original
2012
Earlier work this paper cites.
M. Cacciari, G.P. Salam and G. Soyez, FastJet User Manual , Eur. Phys. J. C 72
Original
2012
Earlier work this paper cites.
J. Casalderrey-Solana, D.C. Gulhan, J.G. Milhano, D. Pablos and K. Rajagopal, A Hybrid Strong/Weak Coupling Approach to Jet Quenching , JHEP 10
Original
2014
Earlier work this paper cites.

Similar

K.C. Zapp, JEWEL 2.0.0: directions for use , Eur. Phys. J. C 74
Original
2014
Cited alongside, same era.
A.J. Larkoski, S. Marzani, G. Soyez and J. Thaler, Soft Drop , JHEP 05
Original
2014
Cited alongside, same era.
D.P. Kingma and J. Ba, Adam: A method for stochastic optimization , arXiv preprint arXiv:1412.6980 (2014)
Original
2014
Cited alongside, same era.
M. Abadi et al., TensorFlow: Large-scale machine learning on heterogeneous systems , 2015
2015
Cited alongside, same era.
F. Chollet et al., “Keras.” https://keras.io , 2015
2015
Cited alongside, same era.

Then

M. Sundararajan, A. Taly and Q. Yan, Axiomatic attribution for deep networks , in International Conference on Machine Learning , pp. 3319–3328, PMLR, 2017
2017
Later among the works it cites.
L. Apolinário, J.G. Milhano, M. Ploskon and X. Zhang, Novel subjet observables for jet quenching in heavy-ion collisions , Eur. Phys. J. C 78
Original
2018
Later among the works it cites.
G. Milhano, U.A. Wiedemann and K.C. Zapp, Sensitivity of jet substructure to jet-induced medium response , Phys. Lett. B 779
Original
2018
Later among the works it cites.
J. Brewer, J.G. Milhano and J. Thaler, Sorting out quenched jets , Phys. Rev. Lett. 122
Original
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

alphaXiv is searching for related work…

L. de Oliveira, M. Kagan, L. Mackey, B. Nachman and A. Schwartzman, Jet-images—deep learning edition , Journal of High Energy Physics 2016

2016

Cited alongside, same era.

R. Kunnawalkam Elayavalli and K.C. Zapp, Medium response in JEWEL and its impact on jet shape observables in heavy ion collisions , JHEP 07

Original

2017

Cited alongside, same era.

R.R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh and D. Batra, Grad-cam: Visual explanations from deep networks via gradient-based localization , in Proceedings of the IEEE international conference on computer vision , pp. 618–626, 2017

2017

Cited alongside, same era.

Y.S. Lai, Automated Discovery of Jet Substructure Analyses , 1810.00835

Original

Cited in the paper.

D. Neill, F. Ringer and N. Sato, Leading jets and energy loss , 2103.16573

Original

Cited in the paper.

Y.-T. Chien and R. Kunnawalkam Elayavalli, Probing heavy ion collisions using quark and gluon jet substructure , 1803.03589

Original

Cited in the paper.

T. Akiba, S. Sano, T. Yanase, T. Ohta and M. Koyama, Optuna: A next-generation hyperparameter optimization framework , in Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining , pp. 2623–2631, 2019

2019

Later among the works it cites.

H.A. Andrews et al., Novel tools and observables for jet physics in heavy-ion collisions , J. Phys. G 47

Original

2020

Later among the works it cites.

A.J. Larkoski, I. Moult and B. Nachman, Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning , Phys. Rept. 841

Original

2020

Later among the works it cites.

A.K. Nain, Model interpretability with integrated gradients , 2020

2020

Later among the works it cites.