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2019

Binary JUNIPR: an interpretable probabilistic model for discrimination

Andreassen, Anders, Feige, Ilya, Frye, Christopher et al.

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JUNIPR is an approach to unsupervised learning in particle physics that scaffolds a probabilistic model for jets around their representation as binary trees.

Separate JUNIPR models can be learned for different event or jet types, then compared and explored for physical insight.
The relative probabilities can also be used for discrimination.
In this paper, we show how the training of the separate models can be refined in the context of classification to optimize discrimination power.

Binary JUNIPR: an interpretable probabilistic model for discrimination · Around

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.
T. S. Roy and A. H. Vijay, “A robust anomaly finder based on autoencoder,” arXiv:1903.02032 [hep-ph]
Original
1903
Earlier work this paper cites.
A. Blance, M. Spannowsky, and P. Waite, “Adversarially-trained autoencoders for robust unsupervised new physics searches,” arXiv:1905.10384 [hep-ph]
Original
1905
Earlier work this paper cites.
S. D. Ellis and D. E. Soper, “Successive combination jet algorithm for hadron collisions,” Phys. Rev
1993
Earlier work this paper cites.
Y. L. Dokshitzer, G. D. Leder, S. Moretti, and B. R. Webber, “Better jet clustering algorithms,” JHEP
1997
Earlier work this paper cites.
D. E. Kaplan, K. Rehermann, M. D. Schwartz, and B. Tweedie, “Top Tagging: A Method for Identifying Boosted Hadronically Decaying Top Quarks,” Phys. Rev. Lett
Original
2008
Earlier work this paper cites.
J. Gallicchio, J. Huth, M. Kagan, M. D. Schwartz, K. Black, and B. Tweedie, “Multivariate discrimination and the Higgs + W/Z search,” JHEP
Original
2011
Earlier work this paper cites.
J. Gallicchio and M. D. Schwartz, “Quark and Gluon Tagging at the LHC,” Phys. Rev. Lett
Original
2011
Earlier work this paper cites.
J. Gallicchio and M. D. Schwartz, “Quark and Gluon Jet Substructure,” JHEP
Original
2013
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980
Original
2014
Earlier work this paper cites.

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