| Table of Contents |
|---|
Author(s)
| Name | Institution | Mail Address | Social Contacts |
|---|---|---|---|
| Lucio Anderlini | INFN Sezione di Firenze | Lucio.Anderlini@fi.infn.it | Hangouts: l.anderlini@gmail.com |
| Matteo Barbetti | Università di Firenze | Matteo.Barbetti@fi.infn.it | N/A |
How to Obtain Support
| Lucio.Anderlini@fi.infn.it | |
| Social | Hangouts: tomboc73 |
| Jira | N/A |
General Information
| ML/DL Technologies | Statistical Learning; Forward Neural Networks |
|---|---|
| Science Fields | High Energy Physics |
| Difficulty | Introductory |
| Language | English |
| Type | fully annotated |
Software and Tools
| Programming Language | Python |
|---|---|
| ML Toolset | Keras + Tensorflow |
| Additional libraries | uproot |
| Suggested Environments | INFN-Cloud VM, bare Linux Node, Google CoLab |
Needed datasets
| Data Creator | LHCb Experiment |
|---|---|
| Data Type | 2011 data |
| Data Size | 1 GB |
| Data Source | CERN OpenData |
Short Description of the Use Case
For the outreach programme LHCb Masterclass students from secondary schools are invited to analyze a sample of D0 → K− pi+ decays as collected from the LHCb experiment to measure the lifetime of the D0 meson.
...
The website of the LHCb International Masterclass, where the excercise is shortly explained can be found at this link.
How to execute it
Requirements
To run this exercise you will need python3, tensorflow 1.x and PyROOT for python3.
Download and run the jupyter notebook: Static jupyer notebook: https://github.com/landerlini/MLINFN-TutorialNotebooks/blob/master/LHCbMasterclassExplained.ipynb
Requirements
To run this exercise you will need python3, tensorflow 1.x and PyROOT for python3.
Contents
With this tutorial, we will introduce the following topics:
...