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Table of Contents


NameInstitutionMail AddressSocial Contacts
Lucio AnderliniINFN Sezione di FirenzeLucio.Anderlini@fi.infn.itHangouts:
Vitaliano CiulliUniversità di FirenzeVitaliano.Ciulli@fi.infn.itN/A

How to Obtain Support
SocialHangouts: l.anderlini

General Information

ML/DL TechnologiesStatistical Learning; Forward Neural Networks
Science FieldsHigh Energy Physics
Typefully annotated

Software and Tools

Programming LanguagePython
ML Toolsetscikit-learn; Keras + Tensorflow
Additional librariesuproot
Suggested EnvironmentsINFN-Cloud VM, bare Linux Node, Google CoLab

Needed datasets

Data CreatorCMS Experiment
Data TypeSimulation
Data Size< 1 GB
Data SourceGoogle Drive

Short Description of the Use Case

Classification is a very common problem in High Energy Physics and historically it has been one of the first to be addressed with machine learning techniques. 


This is the starting point for most statistical analyses in High Energy Physics: one has to develop an algorithm to classify signal and background events using simulation. That algorithm will then be run on real data and the resulting selected events have to be statistically subtracted for the expected contribution from background events in order to count the number of signal events and use them to infer physical properties of the decay. 

How to execute it

Getting started (