# CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks

@article{Paganini2017CaloGANS3,
title={CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks},
author={Michela Paganini and Luke de Oliveira and Benjamin P. Nachman},
journal={ArXiv},
year={2017},
volume={abs/1712.10321}
}
• Published 2017
• Physics, Computer Science, Mathematics
• ArXiv
The precise modeling of subatomic particle interactions and propagation through matter is paramount for the advancement of nuclear and particle physics searches and precision measurements. The most computationally expensive step in the simulation pipeline of a typical experiment at the Large Hadron Collider (LHC) is the detailed modeling of the full complexity of physics processes that govern the motion and evolution of particle showers inside calorimeters. We introduce CaloGAN, a new fast… Expand
156 Citations
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#### References

SHOWING 1-10 OF 69 REFERENCES
Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
• Physics, Mathematics
• 2017
We provide a bridge between generative modeling in the Machine Learning community and simulated physical processes in high energy particle physics by applying a novel Generative Adversarial NetworkExpand
Electron efficiency measurements with the ATLAS detector using 2012 LHC proton–proton collision data
• M. Aaboud, +2,849 authors L. Zwalinski
• Medicine, Physics
• The European physical journal. C, Particles and fields
• 2017
The efficiency to reconstruct and identify electrons at the ATLAS experiment varies from 65 to 95%, depending on the transverse momentum of the electron and background rejection, which is measured in data and evaluated in simulated samples. Expand
Geant4 - A simulation toolkit
Geant4 is a toolkit for simulating the passage of particles through matter. It includes a complete range of functionality including tracking, geometry, physics models and hits. The physics processesExpand
Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit
• Computer Science, Physics
• ArXiv
• 2017
The ability to better recover detailed features from low-signal-to-noise and low angular resolution imaging data significantly increases the ability to study existing data sets of astrophysical objects as well as future observations with observatories such as the Large Synoptic Sky Telescope and the Hubble and James Webb space telescopes. Expand
Fast Simulation of Electromagnetic Showers in the ATLAS Calorimeter: Frozen Showers
One of the most time consuming process simulating pp interactions in the ATLAS detector at LHC is the simulation of electromagnetic showers in the calorimeter. In order to speed up the eventExpand
Electron reconstruction and identification efficiency measurements with the ATLAS detector using the 2011 LHC proton–proton collision data
• G. Aad, +2,889 authors L. Zwalinski
• Physics, Medicine
• The European physical journal. C, Particles and fields
• 2014
The electron reconstruction and identification efficiencies of the ATLAS detector at the LHC have been evaluated using proton–proton collision data collected in 2011 and determined with an accuracy at the few per mil level for electron transverse energy greater than 30 GeV. Expand
Electron performance measurements with the ATLAS detector using the 2010 LHC proton-proton collision data
Detailed measurements of the electron performance of the ATLAS detector at the LHC are reported, using decays of the Z, W and J/ψ particles. Data collected in 2010 at $\sqrt{s}=7\mbox{~TeV}$ areExpand
Reconstruction of three-dimensional porous media using generative adversarial neural networks
• Computer Science, Physics
• Physical review. E
• 2017
Results show that generative adversarial networks can be used to reconstruct high-resolution three-dimensional images of porous media at different scales that are representative of the morphology of the images used to train the neural network. Expand
Final state interactions in the (nuclear) FRITIOF string interaction scenario
• Physics
• 1996
We consider the final state reinteraction of the produced hadrons in a scenario with the initial high energy nuclear interaction provided by the FRITIOF Model. The basic idea is that any producedExpand
The ATLAS Simulation Infrastructure
The simulation software for the ATLAS Experiment at the Large Hadron Collider is being used for large-scale production of events on the LHC Computing Grid. This simulation requires many components,Expand