In each of the 4 sections below, the tutorials are listed roughly in the chronological order you should go
through them. To get started, we suggest you understand all the topics in (2) well, go through the tutorials
relevant to your project in (3), and then jump directly to (4). Tutorials in (1) can be used if/when needed
We suggest you go through this README
before you embark
on your journey.
Also, feel free to look at this
for a complete list
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
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.
(3) Machine Learning (ML)
(4) ML in HEP
part-I: introduces you to "top-tagging", a physics task, that can be tackled by ML techniques.
part-II: explores a more complicated (but hence rewarding) ML approach to tackle the same task,
- 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 architecture called "interaction network."
- Fermilab-LPC-HATS-2020-ML: a similar set
10-notebooks that provides you with a comprehensive guide to the task of jet-classification.