Built on

H. Qu and L. Gouskos, “ParticleNet: Jet Tagging via Particle Clouds,” arXiv:1902.08570 [hep-ph]
Original
1902
Earlier work this paper cites.
A. Butter et al
Original
1902
Earlier work this paper cites.
B. M. Dillon, D. A. Faroughy, and J. F. Kamenik, “Uncovering latent jet substructure,” Phys. Rev
Original
1904
Earlier work this paper cites.
S. Diefenbacher, H. Frost, G. Kasieczka, T. Plehn, and J. M. Thompson, “CapsNets Continuing the Convolutional Quest,” arXiv:1906.11265 [hep-ph]
Original
1906
Earlier work this paper cites.
P. Windischhofer, M. Zgubič, and D. Bortoletto, “Preserving physically important variables in optimal event selections: A case study in Higgs physics,” arXiv:1907.02098 [hep-ph]
Original
1907
Earlier work this paper cites.
S. Wunsch, S. Jörger, R. Wolf, and G. Quast, “Reducing the dependence of the neural network function to systematic uncertainties in the input space,” arXiv:1907.11674 [physics.data-an]
Original
1907
Earlier work this paper cites.
Y.-C. J. Chen, C.-W. Chiang, G. Cottin, and D. Shih, “Boosted W / Z W/Z Tagging with Jet Charge and Deep Learning,” arXiv:1908.08256 [hep-ph]
Original
1908
Earlier work this paper cites.
L. Bradshaw, R. K. Mishra, A. Mitridate, and B. Ostdiek, “Mass Agnostic Jet Taggers,” arXiv:1908.08959 [hep-ph]
Original
1908
Earlier work this paper cites.
E. A. Moreno, T. Q. Nguyen, J.-R. Vlimant, O. Cerri, H. B. Newman, A. Periwal, M. Spiropulu, J. M. Duarte, and M. Pierini, “Interaction networks for the identification of boosted H → b b ¯ H\to b\overline{b} decays,” arXiv:1909.12285 [hep-ex]
Original
1909
Earlier work this paper cites.
S. A. Dudani, “The distance-weighted k-nearest-neighbor rule,” IEEE Transactions on Systems, Man, and Cybernetics
1976
Earlier work this paper cites.
G. J. Székely, M. L. Rizzo, and N. K. Bakirov, “Measuring and testing dependence by correlation of distances,” Ann. Statist
2007
Earlier work this paper cites.
T. Sjostrand, S. Mrenna, and P. Z. Skands, “A Brief Introduction to PYTHIA 8.1,” Comput. Phys. Commun
Original
2008
Earlier work this paper cites.
M. Cacciari, G. P. Salam, and G. Soyez, “The anti- k t k_{t} jet clustering algorithm,” JHEP
Original
2008
Earlier work this paper cites.
G. J. Székely and M. L. Rizzo, “Brownian distance covariance,” Ann. Appl. Stat
2009
Earlier work this paper cites.
D. Krohn, J. Thaler, and L.-T. Wang, “Jet Trimming,” JHEP
Original
2010
Earlier work this paper cites.
J. Thaler and K. Van Tilburg, “Identifying Boosted Objects with N-subjettiness,” JHEP
Original
2011
Earlier work this paper cites.
R. Li, W. Zhong, and L. Zhu, “Feature screening via distance correlation learning,” Journal of the American Statistical Association
2012
Earlier work this paper cites.
G. J. Székely and M. L. Rizzo, “On the uniqueness of distance covariance,” Statistics & Probability Letters
2012
Earlier work this paper cites.

Similar

M. Cacciari, G. P. Salam, and G. Soyez, “FastJet User Manual,” Eur. Phys. J
Original
2012
Cited alongside, same era.
J. Freeman, J. D. Lewis, W. Ketchum, S. Poprocki, A. Pronko, V. Rusu, and P. Wittich, “An Artificial neural network based b b jet identification algorithm at the CDF Experiment,” Nucl. Instrum. Meth
Original
2012
Cited alongside, same era.
G. J. Székely and M. L. Rizzo, “The distance correlation t-test of independence in high dimension,” J. Multivar. Anal
2013
Cited alongside, same era.
J. Stevens and M. Williams, “uBoost: A boosting method for producing uniform selection efficiencies from multivariate classifiers,” JINST
Original
2013
Cited alongside, same era.
G. J. Székely and M. L. Rizzo, “Partial distance correlation with methods for dissimilarities,” Ann. Statist

Then

C. Shimmin, P. Sadowski, P. Baldi, E. Weik, D. Whiteson, E. Goul, and A. Søgaard, “Decorrelated Jet Substructure Tagging using Adversarial Neural Networks,” Phys. Rev
Original
2017
Later among the works it cites.
S. Macaluso and D. Shih, “Pulling Out All the Tops with Computer Vision and Deep Learning,” JHEP
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,” SciPost Phys
Original
2018
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,” JHEP
Original
2018
Later among the works it cites.
K. Fraser and M. D. Schwartz, “Jet Charge and Machine Learning,” JHEP

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…

2020

DisCo Fever: Robust Networks Through Distance Correlation

Kasieczka, Gregor, Shih, David

Understand

While deep learning has proven to be extremely successful at supervised classification tasks at the LHC and beyond, for practical applications, raw classification accuracy is often not the only consideration.

One crucial issue is the stability of network predictions, either versus changes of individual features of the input data, or against systematic perturbations.
We present a new method based on a novel application of "distance correlation" (DisCo), a measure quantifying non-linear correlations, that achieves equal performance to state-of-the-art adversarial decorrelation networks but is much simpler and more stable to train.
To demonstrate the effectiveness of our method, we carefully recast a recent ATLAS study of decorrelation methods as applied to boosted, hadronic W-tagging.