Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics

Using detailed simulations of calorimeter showers as training data, we investigate the use of deep learning algorithms for the simulation and reconstruction of single isolated particles produced in high-energy physics collisions. We train neural networks on single-particle shower data at the calorim...

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Detalles Bibliográficos
Autores principales: Belayneh, Dawit, Carminati, Federico, Farbin, Amir, Hooberman, Benjamin, Khattak, Gulrukh, Liu, Miaoyuan, Liu, Junze, Olivito, Dominick, Barin Pacela, Vitória, Pierini, Maurizio, Schwing, Alexander, Spiropulu, Maria, Vallecorsa, Sofia, Vlimant, Jean-Roch, Wei, Wei, Zhang, Matt
Lenguaje:eng
Publicado: 2019
Materias:
hep-ex
Particle Physics - Experiment
cs.LG
Computing and Computers
cs.CV
physics.ins-det
Detectors and Experimental Techniques
Acceso en línea:https://dx.doi.org/10.1140/epjc/s10052-020-8251-9
http://cds.cern.ch/record/2706000

Internet

https://dx.doi.org/10.1140/epjc/s10052-020-8251-9
http://cds.cern.ch/record/2706000

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