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Original
1901
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Original
1901
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Original
1903
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S. Bollweg, M. Haußmann, G. Kasieczka, M. Luchmann, T. Plehn, and J. Thompson, Deep-Learning Jets with Uncertainties and More
Original
1904
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Sherpa, E. Bothmann et al
Original
1905
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A. Butter, T. Plehn, and R. Winterhalder, How to GAN LHC Events
Original
1907
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L. Ardizzone, C. Lüth, J. Kruse, C. Rother, and U. Köthe, Guided image generation with conditional invertible neural networks
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A. Butter, T. Plehn, and R. Winterhalder, How to GAN LHC Events
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1907
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I. Kobyzev, S. Prince, and M. A. Brubaker, Normalizing flows: An introduction and review of current methods
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1908
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S. Carrazza and F. A. Dreyer, Lund jet images from generative and cycle-consistent adversarial networks
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1909
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B. Nachman, A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
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2020

Generative Networks for LHC events

Butter, Anja, Plehn, Tilman

Understand

LHC physics crucially relies on our ability to simulate events efficiently from first principles.

Modern machine learning, specifically generative networks, will help us tackle simulation challenges for the coming LHC runs.
Such networks can be employed within established simulation tools or as part of a new framework.
Since neural networks can be inverted, they also open new avenues in LHC analyses.