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Training and statistics

Aside from the physic analysis, we share a set of useful resources to engage with the ATLAS open data.

Jupyter Notebooks​

Python, ROOT/C++​

Statistics tutorial​

This notebook will allow you to go through the typical steps of a statistical analysis in high energy physics at particle colliders, inspecting the way to extract quantitative information from colision data. There are two main ways to run this tutorial: Binder and SWAN (the latter requires a CERN account).
Physics: ⭐
Coding: ⭐⭐
Time: ⭐⭐⭐

Kaggle Notenooks​

Python​

Model fitting​

This notebook uses ATLAS Open Data to show you some steps to prepare for model fitting.
Physics: ⭐
Coding: ⭐⭐
Time: ⭐

Monte Carlo weights​

This notebook uses ATLAS Open Data to show you how to prepare MC weights on your way to Machine Learning.
Physics: ⭐⭐
Coding: ⭐⭐
Time: ⭐⭐

Uproot​

Uproot Tutorial​

This tutorial aims to demonstrate how to quickly get started with Uproot, a Python package that can read and write files in the .root format without actually requiring or running the ROOT software at all (as opposed to PyROOT, which is just an interface in Python that runs ROOT behind the scenes).
Physics: ⭐
Coding: ⭐⭐
Time: ⭐⭐⭐

Matplotlib for HEP​

This training module introduces matplotlib and creates plots commonly used in HEP. It also introduces mplhep, a plotting library designed specifically for HEP plots.
Physics: ⭐
Coding: ⭐⭐
Time: ⭐⭐⭐

Introduction to Machine Learning​

This tutorial explores Machine Learning using ATLAS Open Data and scikit-learn and PyTorch for applications in high energy physics.
Physics: ⭐
Coding: ⭐⭐
Time: ⭐⭐⭐

Machine Learning on GPU​

This tutorial explores Machine Learning using GPU-enabled PyTorch for applications in high energy physics.
Physics: ⭐
Coding: ⭐⭐
Time: ⭐⭐⭐

Local disk or virtual machines​

ROOT/C++​

Dataframe tutorials​

These examples are taken by the official ROOT reference guide and show various features of RDataFrame: ROOT's declarative analysis interface. RDataFrame offers a high level interface for the analysis of data stored in TTrees, CSV files and other data formats. In addition, multi-threading and other low-level optimisations allow users to exploit all the resources available on their machines transparently.
Physics: ⭐
Coding: ⭐⭐⭐
Time: ⭐