CMS Machine Learning Data Analsyis School (DAS) Short Exercise

Introduction

This is a modified version of this tutorial.

This is a set of tutorials for the CMS Machine Learning Data Analysis School (DAS) Short Exercise. They are intended to show you how to build machine learning models in python (Keras/TensorFlow) and use them in your ROOT-based analyses. We will build event-level classifiers for differentiating VBF Higgs and standard model background 4 muon events and jet-level classifiers for differentiating boosted W boson jets from QCD jets.

Main notebooks in this tutorial

1. 1.1-datasets-uproot.ipynb: reading/writing datasets from ROOT files with uproot
2. 2-plotting-ROOT.ipynb: plotting with pyROOT
3. 2-plotting-python.ipynb: plotting with matplotlib
4. 3-dense.ipynb: building, training, and evaluating a fully connected (dense) neural network in Keras
5. 4-preprocessing.ipynb: preprocessing CMS open data to build jet-images (optional)
6. 5-conv2d.ipynb: building, training, and evaluating a 2D convolutional neural network in Keras

Setup

For all the notebooks, you need to activate the kernel python-analysis-env, except the 2-plotting-ROOT.ipynb which needs the kernel root-analysis-env.

Links

The indico page is: https://indico.cern.ch/e/cmsdas2022

The Mattermost for live support is: https://mattermost.web.cern.ch/cmsdaslpc2022/channels/shortexml

The twiki is: https://twiki.cern.ch/twiki/bin/view/CMS/SWGuideCMSDataAnalysisSchoolLPC2022MachineLearningShortExercise