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2006
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2008
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M. Cacciari, G. P. Salam and G. Soyez, The Anti-k(t) jet clustering algorithm
2008
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D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming
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P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio and P.-A. Manzagol, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
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D. Scherer, A. Muller and S. Behnke, Evaluation of pooling operations in convolutional architectures for object recognition
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J. Gallicchio and M. D. Schwartz, Seeing in Color: Jet Superstructure
Original
2010
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J. Thaler and K. Van Tilburg, Identifying Boosted Objects with N-subjettiness
Original
2011
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2015

Jet-Images -- Deep Learning Edition

de Oliveira, Luke, Kagan, Michael, Mackey, Lester et al.

Understand

Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning architectures to identify highly boosted W bosons.

Modern deep learning algorithms trained on jet images can out-perform standard physically-motivated feature driven approaches to jet tagging.
We develop techniques for visualizing how these features are learned by the network and what additional information is used to improve performance.
This interplay between physically-motivated feature driven tools and supervised learning algorithms is general and can be used to significantly increase the sensitivity to discover new particles and new forces, and gain a deeper understanding of the physics within jets.

Built on

Y. Nesterov, A method of solving a convex programming problem with convergence rate O(1/sqr(k))
1983
Earlier work this paper cites.
T. Sjostrand, S. Mrenna and P. Z. Skands, PYTHIA 6.4 Physics and Manual
2006
Earlier work this paper cites.
T. Sjostrand, S. Mrenna and P. Z. Skands, A Brief Introduction to PYTHIA 8.1
Original
2008
Earlier work this paper cites.
M. Cacciari, G. P. Salam and G. Soyez, The Anti-k(t) jet clustering algorithm
2008
Earlier work this paper cites.
D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming
Original
2010
Earlier work this paper cites.
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio and P.-A. Manzagol, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
2010
Earlier work this paper cites.
D. Scherer, A. Muller and S. Behnke, Evaluation of pooling operations in convolutional architectures for object recognition
2010
Earlier work this paper cites.
J. Gallicchio and M. D. Schwartz, Seeing in Color: Jet Superstructure
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.