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Emulating the impact of additional proton–proton interactions in the ATLAS simulation by presampling sets of inelastic Monte Carlo events

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Emulating the impact of additional proton–proton interactions in the ATLAS simulation by presampling sets of inelastic Monte Carlo events. / ATLAS Collaboration.
In: Computing and Software for Big Science, Vol. 6, No. 1, 3, 27.01.2022.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

ATLAS Collaboration 2022, 'Emulating the impact of additional proton–proton interactions in the ATLAS simulation by presampling sets of inelastic Monte Carlo events', Computing and Software for Big Science, vol. 6, no. 1, 3. https://doi.org/10.1007/s41781-021-00062-2

APA

ATLAS Collaboration (2022). Emulating the impact of additional proton–proton interactions in the ATLAS simulation by presampling sets of inelastic Monte Carlo events. Computing and Software for Big Science, 6(1), Article 3. https://doi.org/10.1007/s41781-021-00062-2

Vancouver

ATLAS Collaboration. Emulating the impact of additional proton–proton interactions in the ATLAS simulation by presampling sets of inelastic Monte Carlo events. Computing and Software for Big Science. 2022 Jan 27;6(1):3. doi: 10.1007/s41781-021-00062-2

Author

ATLAS Collaboration. / Emulating the impact of additional proton–proton interactions in the ATLAS simulation by presampling sets of inelastic Monte Carlo events. In: Computing and Software for Big Science. 2022 ; Vol. 6, No. 1.

Bibtex

@article{e64c6a294e3943acbc6ae19bd7b0917d,
title = "Emulating the impact of additional proton–proton interactions in the ATLAS simulation by presampling sets of inelastic Monte Carlo events",
abstract = "AbstractThe accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hard scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy.",
keywords = "Nuclear and High Energy Physics, Computer Science (miscellaneous), Software",
author = "{ATLAS Collaboration} and A.E. Barton and I.A. Bertram and G. Borissov and E.V. Bouhova-Thacker and H. Fox and R.C.W. Henderson and R.W.L. Jones and V. Kartvelishvili and R.E. Long and P.A. Love and D. Muenstermann and Izaac Sanderswood and M. Smizanska and A.S. Tee and A.M. Wharton and Melissa Yexley",
year = "2022",
month = jan,
day = "27",
doi = "10.1007/s41781-021-00062-2",
language = "English",
volume = "6",
journal = "Computing and Software for Big Science",
issn = "2510-2036",
publisher = "Springer Science and Business Media LLC",
number = "1",

}

RIS

TY - JOUR

T1 - Emulating the impact of additional proton–proton interactions in the ATLAS simulation by presampling sets of inelastic Monte Carlo events

AU - ATLAS Collaboration

AU - Barton, A.E.

AU - Bertram, I.A.

AU - Borissov, G.

AU - Bouhova-Thacker, E.V.

AU - Fox, H.

AU - Henderson, R.C.W.

AU - Jones, R.W.L.

AU - Kartvelishvili, V.

AU - Long, R.E.

AU - Love, P.A.

AU - Muenstermann, D.

AU - Sanderswood, Izaac

AU - Smizanska, M.

AU - Tee, A.S.

AU - Wharton, A.M.

AU - Yexley, Melissa

PY - 2022/1/27

Y1 - 2022/1/27

N2 - AbstractThe accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hard scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy.

AB - AbstractThe accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hard scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy.

KW - Nuclear and High Energy Physics

KW - Computer Science (miscellaneous)

KW - Software

U2 - 10.1007/s41781-021-00062-2

DO - 10.1007/s41781-021-00062-2

M3 - Journal article

VL - 6

JO - Computing and Software for Big Science

JF - Computing and Software for Big Science

SN - 2510-2036

IS - 1

M1 - 3

ER -