Open Access
EPJ Web of Conferences, volume 214, pages 2034
Generative Models for Fast Calorimeter Simulation.LHCb case
2
Yandex School of Data Analysis, Moscow, Russia
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Publication type: Journal Article
Publication date: 2019-09-17
Journal:
EPJ Web of Conferences
Quartile SCImago
— Quartile WOS
—
Impact factor: —
ISSN: 21016275, 2100014X
General Engineering
General Environmental Science
General Earth and Planetary Sciences
Abstract
Simulation is one of the key components in high energy physics. Historically it relies on the Monte Carlo methods which require a tremendous amount of computation resources. These methods may have difficulties with the expected High Luminosity Large Hadron Collider (HL-LHC) needs, so the experiments are in urgent need of new fast simulation techniques. We introduce a new Deep Learning framework based on Generative Adversarial Networks which can be faster than traditional simulation methods by 5 orders of magnitude with reasonable simulation accuracy. This approach will allow physicists to produce a sufficient amount of simulated data needed by the next HL-LHC experiments using limited computing resources.
Citations by journals
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8
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Physical Review D
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Physical Review D
8 publications, 25.81%
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Journal of Instrumentation
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Journal of Instrumentation
5 publications, 16.13%
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Journal of Physics: Conference Series
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Journal of Physics: Conference Series
4 publications, 12.9%
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EPJ Web of Conferences
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EPJ Web of Conferences
4 publications, 12.9%
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European Physical Journal C
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European Physical Journal C
2 publications, 6.45%
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Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
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Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
1 publication, 3.23%
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Nature Reviews Physics
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Nature Reviews Physics
1 publication, 3.23%
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Multimedia Tools and Applications
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Multimedia Tools and Applications
1 publication, 3.23%
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IEEE Access
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IEEE Access
1 publication, 3.23%
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SciPost Physics
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SciPost Physics, 1, 3.23%
SciPost Physics
1 publication, 3.23%
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Doklady Mathematics
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Doklady Mathematics
1 publication, 3.23%
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Computing and Software for Big Science
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Computing and Software for Big Science
1 publication, 3.23%
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8
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Citations by publishers
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9
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IOP Publishing
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IOP Publishing
9 publications, 29.03%
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American Physical Society (APS)
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American Physical Society (APS)
8 publications, 25.81%
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Springer Nature
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Springer Nature
5 publications, 16.13%
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EDP Sciences
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EDP Sciences
4 publications, 12.9%
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Elsevier
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Elsevier
1 publication, 3.23%
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IEEE
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IEEE
1 publication, 3.23%
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Stichting SciPost
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Stichting SciPost, 1, 3.23%
Stichting SciPost
1 publication, 3.23%
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Pleiades Publishing
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Pleiades Publishing
1 publication, 3.23%
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- We do not take into account publications that without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
- Statistics recalculated weekly.
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Chekalina V. et al. Generative Models for Fast Calorimeter Simulation.LHCb case // EPJ Web of Conferences. 2019. Vol. 214. p. 2034.
GOST all authors (up to 50)
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Chekalina V., Orlova E. G., Ratnikov F., Ulyanov D., Ustyuzhanin A., Zakharov E. Generative Models for Fast Calorimeter Simulation.LHCb case // EPJ Web of Conferences. 2019. Vol. 214. p. 2034.
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TY - JOUR
DO - 10.1051/epjconf/201921402034
UR - https://doi.org/10.1051%2Fepjconf%2F201921402034
TI - Generative Models for Fast Calorimeter Simulation.LHCb case
T2 - EPJ Web of Conferences
AU - Chekalina, Viktoria
AU - Ratnikov, Fedor
AU - Ustyuzhanin, Andrey
AU - Zakharov, Egor
AU - Orlova, E. G.
AU - Ulyanov, Dmitry
PY - 2019
DA - 2019/09/17 00:00:00
PB - EDP Sciences
SP - 2034
VL - 214
SN - 2101-6275
SN - 2100-014X
ER -
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@article{2019_Chekalina,
author = {Viktoria Chekalina and Fedor Ratnikov and Andrey Ustyuzhanin and Egor Zakharov and E. G. Orlova and Dmitry Ulyanov},
title = {Generative Models for Fast Calorimeter Simulation.LHCb case},
journal = {EPJ Web of Conferences},
year = {2019},
volume = {214},
publisher = {EDP Sciences},
month = {sep},
url = {https://doi.org/10.1051%2Fepjconf%2F201921402034},
pages = {2034},
doi = {10.1051/epjconf/201921402034}
}