# Particle identification with an electromagnetic calorimeter using a Convolutional Neural Network

- Institution: CERN
- Department: Conferences
- Speaker: Alex Rua Herrera
- Year: 2021
- Language: English
- Subjects: Physics, Computer science, Earth and environment
- Source: [CERN](https://videos.cern.ch/record/3007968)
- Licence: [CERN](http://copyright.web.cern.ch)
- Who can watch: Tweens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

Based on the fact that showers in calorimeters depend on the type of particle, this note attempts to perform a particle classifier for electromagnetic and hadronic particles on an electromagnetic calorimeter, based on the energy deposit of individual cells. Using data from a Geant4 simulation of a proposal of a Crystal Fiber Calorimeter (SPACAL), foreseen for a future upgrade of the LHCb detector, a classifier is built using Convolutional Neural Networks. Results obtained demonstrate that the higher resolution of this ECAL allows to attain over 95% precision in some classifications such as photons against neutrons..

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## Lectures

1. [Particle identification with an electromagnetic calorimeter using a Convolutional Neural Network](https://invisiblenetwork.ai/learn/cern/3007968/9245a95b3fc9), 13 min (CERN's player)