HEPML Resources

Table of contents

• General ML Info
• Software
• Lectures
• Papers
• Workshops
• Tweets
• Contributing

Listing of useful (mostly) public learning resources for machine learning applications in high energy physics (HEPML). Listings will be in reverse chronological order (like a CV).

N.B.: This listing will almost certainly be biased towards work done by ATLAS scientists, as the maintainer is a member of ATLAS and so sees ATLAS work the most. However, this is not the desired case and help to diversify this listing would be greatly appreciated.

General Machine Learning Information

Lectures and Tutorials

• CERN Academic Training Lecture Regular Programme, April 2017 (Machine Learning):

  • Machine Learning (Lecture 1) --- Michael Kagan (SLAC)
  • Machine Learning (Lecture 2) --- Michael Kagan (SLAC)
  • Deep Learning and Vision --- Jonathon Shlens (Google Research)

• Scikit-learn Tutorial, Gilles Louppe

• Keras and TMVA interfaces Tutorial, Stefan Wunsch

• Introduction to Deep Learning with Keras Tutorial, Luke de Oliveira

• Deep Learning in High Energy Physics, Amir Farbin

Courses

• Applications of Deep Learning to High Energy Physics, Amir Farbin (Spring, 2017 - University of Texas at Arlington)

Journals

• Distill Research Journal

Software

Common software tools and environments used in HEP for ML

• The Conda package and environment manager and Anaconda Python library collection

  • Using ROOT/PyROOT with Conda and NumPy

• scikit-learn

• TMVA

High level deep learning libraries/framework APIs

• Keras

Deep learning frameworks

• TensorFlow
• Theano

HEP Developed Inference Tools

• LWTNN

Lectures

• QCD-Aware Neural Networks for Jet Physics, Kyle Cranmer
• (Machine) Learning to Pivot with Adversarial Networks, Gilles Louppe

Lecture and Seminar Series

• CERN Data Science Seminars

Papers

• M. Paganini, L. de Oliveira, and B. Nachman, "CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks," arXiv:1705.02355 [hep-ex]. (May 5, 2017)

• C. Shimmin, P. Sadowski, P. Baldi, E. Weik, D. Whiteson, E. Goul, and A. Sgaard, "Decorrelated Jet Substructure Tagging using Adversarial Neural Networks," arXiv:1703.03507 [hep-ex]. (March 9, 2017)

• G. Louppe, K. Cho, C. Becot, and K. Cranmer, "QCD-Aware Recursive Neural Networks for Jet Physics," arXiv:1702.00748 [hep-ph]. (February 2, 2017)

• L. de Oliveira, M. Paganini, and B. Nachman, "Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis," arXiv:1701.05927 [stat.ML]. (January 20, 2017)

• P. T. Komiske, E. M. Metodiev, and M. D. Schwartz, "Deep learning in color: towards automated quark/gluon jet discrimination," JHEP 01 (2017) 110, arXiv:1612.01551 [hep-ph]. (December 5, 2016)

• MicroBooNE Collaboration, R. Acciarri et al., "Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber," JINST 12 (2017) no. 03, P03011, arXiv:1611.05531 [physics.ins-det]. (November 16, 2016)

• M. Kagan, L. d. Oliveira, L. Mackey, B. Nachman, and A. Schwartzman, "Boosted Jet Tagging with Jet-Images and Deep Neural Networks," EPJ Web Conf. 127 (2016) 00009. (November 15, 2016)

• G. Bertone, M. P. Deisenroth, J. S. Kim, S. Liem, R. Ruiz de Austri, and M. Welling, "Accelerating the BSM interpretation of LHC data with machine learning," arXiv:1611.02704 [hep-ph]. (November 8, 2016)

• G. Louppe, M. Kagan, and K. Cranmer, "Learning to Pivot with Adversarial Networks," arXiv:1611.01046 [stat.ME]. (November 3, 2016)

• J. Barnard, E. N. Dawe, M. J. Dolan, and N. Rajcic, "Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks," Phys. Rev. D95 (2017) no. 1, 014018, arXiv:1609.00607 [hep-ph] (September 2, 2016)

• A. Aurisano, A. Radovic, D. Rocco, A. Himmel, M. D. Messier, E. Niner, G. Pawloski, F. Psihas, A. Sousa, and P. Vahle, "A Convolutional Neural Network Neutrino Event Classifier," JINST 11 (2016) no. 09, P09001, arXiv:1604.01444 [hep-ex]. (April 5, 2016)

• L. de Oliveira, M. Kagan, L. Mackey, B. Nachman, and A. Schwartzman, "Jet-images deep learning edition," JHEP 07 (2016) 069, arXiv:1511.05190 [hep-ph]. (November 16, 2015)

• P. Baldi, P. Sadowski, and D. Whiteson, "Searching for Exotic Particles in High-Energy Physics with Deep Learning," Nature Commun. 5 (2014) 4308, arXiv:1402.4735 [hep-ph]. (February 19, 2014)

Workshops

Upcoming

• Hammers & Nails - Machine Learning & HEP (July 19-28, 2017)
• Third Machine Learning in High Energy Physics Summer School 2017 (July 17-23, 2017)
• First Computational and Data Science School for High Energy Physics (July 10-13, 2017)

Past

• ATLAS Machine Learning Workshop (2017) (June 6-9, 2017)
• Workshop on Machine Learning and b-tagging (May 23-26, 2017)
• DS@HEP 2017 (May 8-12, 2017)
• 2nd S2I2 HEP/CS Workshop (Parallel Session) (May 1-3, 2017)
• CERN openlab workshop on Machine Learning and Data Analytics (April 27, 2017)
• First IML Workshop on Machine Learning (March 20-22, 2017)
• DS@HEP at the Simons Foundation (July 5-7, 2016)
• Second Machine Learning in High Energy Physics Summer School 2016 (June 20-26, 2016)
• ALICE Mini-Workshop 2016: Statistical Methods and Machine Learning Tutorial (May 18, 2016)
• ATLAS Machine Learning Workshop (2016) (March 29-31, 2016)
• Data Science @ LHC 2015 (November 9-13, 2015)

Tweets

• #HEPML collection of tweets

Contributing

Contributions to help improve the listing are very much welcome! Please read CONTRIBUTING.md for details on the process for submitting pull requests or filing issues.

Authors

Listing maintainer: Matthew Feickert

Acknowledgments

• Following PurpleBooth's README style
• All badges made by shields.io
• Inspiration for this listing came from the Awesome Machine Learning repo