Skip to content
Listing of useful learning resources for machine learning applications in high energy physics (HEPML)
TeX Makefile
Branch: master
Clone or download

README.md

HEPML Resources

DOI license

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.

Table of contents

Introductory Material Introductory

Lectures

Seminar Series

Tutorials

Schools

HEP-ML:

Upcoming:
Past:

Deep Learning:

Upcoming:
Past:

Courses

Journals

Software

Common software tools and environments used in HEP for ML

High level deep learning libraries/framework APIs

Deep learning frameworks

HEP to ML bridge tools

  • lwtnn: Tool to run Keras networks in C++ code

  • sklearn-porter: Transpile trained scikit-learn estimators to C, Java, JavaScript and others

  • ONNX open format to represent deep learning models

  • Scikit-HEP: Toolset of interfaces and Python tools for Particle Physics

    • root_numpy: The interface between ROOT and numpy

    • root_pandas: An upgrade of root_numpy to use with pandas

    • uproot: Mimimalist ROOT to numpy converter (no dependency on ROOT)

  • ttree2hdf5: Mimimalist ROOT to HDF5 converter (written in C++)

  • hep_ml: Python algorithms and tools for HEP ML use cases

Images for Containerized Environments

Public Datasets

Papers

A .bib file for all papers listed is available in the tex directory.

A listing of papers of applications of machine learning to high energy physics can be found in papers.md.

Workshops

Upcoming

Past

Tweets

People

  • HEPML directory: Opt-in list of people working at the intersection of Machine Learning and High Energy Physics

Other HEP Resource Collections

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

You can’t perform that action at this time.