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A. Butter et al
Original
1902
Earlier work this paper cites.
H. Qu and L. Gouskos, ParticleNet: Jet Tagging via Particle Clouds
Original
1902
Earlier work this paper cites.
S. Bollweg, M. Haußmann, G. Kasieczka, M. Luchmann, T. Plehn, and J. Thompson, Deep-Learning Jets with Uncertainties and More
Original
1904
Earlier work this paper cites.
S. Diefenbacher, H. Frost, G. Kasieczka, T. Plehn, and J. M. Thompson, CapsNets Continuing the Convolutional Quest
Original
1906
Earlier work this paper cites.
S. Carrazza and J. Cruz-Martinez, Towards a new generation of parton densities with deep learning models
Original
1907
Earlier work this paper cites.
A. Butter, T. Plehn, and R. Winterhalder, How to GAN LHC Events
Original
1907
Earlier work this paper cites.
J. Brehmer, F. Kling, I. Espejo, and K. Cranmer, MadMiner: Machine learning-based inference for particle physics
Original
1907
Earlier work this paper cites.
J. Brehmer, S. Dawson, S. Homiller, F. Kling, and T. Plehn, Benchmarking simplified template cross sections in W H WH production
Original
1908
Earlier work this paper cites.
A. Andreassen, P. T. Komiske, E. M. Metodiev, B. Nachman, and J. Thaler, OmniFold: A Method to Simultaneously Unfold All Observables
Original
1911
Earlier work this paper cites.
A. Butter, T. Plehn, and R. Winterhalder, How to GAN Event Subtraction
Original
1912
Earlier work this paper cites.
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, and R. Winterhalder, How to GAN away Detector Effects
Original
1912
Earlier work this paper cites.
E. Bothmann, T. Janßen, M. Knobbe, T. Schmale, and S. Schumann, Exploring phase space with Neural Importance Sampling
Original
2001
Earlier work this paper cites.
G. Kasieczka, M. Luchmann, F. Otterpohl, and T. Plehn, Per-Object Systematics using Deep-Learned Calibration
Original
2003
Earlier work this paper cites.
F. A. Di Bello, S. Ganguly, E. Gross, M. Kado, M. Pitt, L. Santi, and J. Shlomi, Towards a Computer Vision Particle Flow
Original
2003
Earlier work this paper cites.
B. P. Roe, H.-J. Yang, J. Zhu, Y. Liu, I. Stancu, and G. McGregor, Boosted decision trees, an alternative to artificial neural networks
2005
Earlier work this paper cites.
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, A. Rousselot, R. Winterhalder, L. Ardizzone, and U. Köthe, Invertible Networks or Partons to Detector and Back Again
Original
2006
Earlier work this paper cites.

Similar

S. Forte and S. Carrazza, Parton distribution functions
Original
2008
Cited alongside, same era.
A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman, and T. Plehn, GANplifying event samples
Original
2008
Cited alongside, same era.
B. Stienen and R. Verheyen, Phase space sampling and inference from weighted events with autoregressive flows
Original
2011
Cited alongside, same era.
T. Plehn, Lectures on LHC Physics
Original
2012
Cited alongside, same era.
M. Backes, A. Butter, T. Plehn, and R. Winterhalder, How to GAN Event Unweighting
Original
2012

Then

P. T. Komiske, E. M. Metodiev, and J. Thaler, Energy Flow Networks: Deep Sets for Particle Jets
Original
2019
Later among the works it cites.
J. Aylett-Bullock, S. Badger, and R. Moodie, Optimising simulations for diphoton production at hadron colliders using amplitude neural networks
Original
2021
Later among the works it cites.
V. Mikuni and F. Canelli, Point cloud transformers applied to collider physics
Original
2021
Later among the works it cites.
B. M. Dillon, T. Plehn, C. Sauer, and P. Sorrenson, Better Latent Spaces for Better Autoencoders
Original
2021
Later among the works it cites.
G. Kasieczka et al
Original

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2022

Modern Machine Learning for LHC Physicists

Plehn, Tilman, Butter, Anja, Dillon, Barry et al.

Understand

Depending on the point of view, modern machine learning is either providing an unprecedented boost to the numerical methods of particle physics, or it is transforming the way we do science with vast amounts of complex data.

In any case, it is crucial for young researchers to stay on top of this development and apply cutting-edge methods and tools to all LHC physics tasks.
These lecture notes lead students with basic knowledge of particle physics and significant enthusiasm for machine learning to relevant applications.
They start with an LHC-specific motivation and a non-standard introduction to neural networks and then cover classification, unsupervised classification, generative networks, data representations, and inverse problems.