Pulse Brain · Growing Health Evidence Index
Peer-reviewed

New approaches using machine learning for fast shower simulation in ATLAS

A. Hasib, J. Schaarschmidt, S. Gadatsch, T. Golling, Dalila Salamani, A. Ghosh, D. Rousseau, K. Cranmer, G. A. Stewart, Gilles Louppe

CERN Bulletin · 2018

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Summary

Modeling the detector response to collisions is one of the most CPU expensive and time-consuming aspects in the LHC. The current ATLAS baseline, GEANT4, is highly CPU intensive. With the large collision dataset expected in the future, CPU usage becomes critical. During the LHC Run-1, a fast calorimeter simulation (FastCaloSim) was successfully used by ATLAS. FastCaloSim parametrizes the energy response of particles in the calorimeter cells, accounting for the lateral shower profile and the correlation of the energy deposition among various calorimeter layers. It significantly speeds up the calorimeter simulation. An improved version of FastCaloSim is currently under development to reduce CPU and memory requirements and to improve the physics description. The new FastCaloSim implements mach

Source type
Peer-reviewed study
Catalogue ID
BFmokjnw0q-nsblzi
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