Storage is one of the main limiting factors to the recording of information from proton-proton collision events at the Large Hadron Collider at CERN. This project aims to implement an autoencoder based deep-compression algorithm on an ATLAS trigger machine to reduce this storage requirement and improve accessibility time of the collision data captured by a trigger system. We also plan to design an autoencoder model that has a better compression factor and adequate execution time and memory requirements in order to be deployed on a trigger system.

Organization

Student

Honey Gupta

Mentors

  • Lukas Heinrich
  • Antonio Boveia
  • Baptiste Ravina
  • Rebeca Gonzalez Suarez
  • Caterina Doglioni
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2020