In each of the 3 sections below, the tutorials are ordered by difficulty (easiest to hardest) in our own view. To get started, we suggest you start with (2) and once you finish a few of these tutorials jump directly to (3).
We suggest you go through this README
before you embark on your journey.
Also, feel free to look at this
for a complete list of ML activities in particle physics.
(1) Particle Physics (HEP)
- CMS related terminology: this twiki probably contains everything there is to know about CMS. We suggest you skip it for now but always know that it's there for you :)
- uproot tutorial: a python package that can read and write files in the .root format without actually requiring or running the ROOT software at all.
- ROOT tutorial: an open-source data analysis framework used in HEP, which lets you save and access your experiment’s data, allows you to process the data in a computationally efficient and statistically sound way and gives you access to all tools to produce publication-quality results. Basically it is C++ with many predefined classes and function.
- Fermilab-LPC-HATS: a set of tutorials covering different aspects of HEP. Topics include: MET, Trigger, jet-algorithms, etc.
(2) Machine Learning (ML)
(3) ML in HEP
- Top-tagging part-I: introduces you to "top-tagging", a physics task, that can be tackled by ML techniques.
- Top-tagging part-II: explores a more complicated (but hence rewarding) ML approach to tackle the same task, top-tagging.
- CMSDAS-2020-ML: a similar run through top-tagging.
- UCSD particle physics and ML data science capstone project: a set of 9-week material (8-notebooks) that guides you to the task of jet-classification. It takes you from data exploration to building classifiers to making the classifiers robust to improving those classifiers by using a GNN architecure called "InteractionNetwork".
- Fermilab-LPC-HATS-2020-ML: a similar set of 10-notebooks that provides you with a comprehensive guide to the task of jet-classification.