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

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

In: Computing and Software for Big Science, Vol. 6, No. 1, 3, 01.2022.

Research output: Contribution to journal › Article › peer-review

BibTeX

@article{7f0718095a9e4408a5913bc80199c630,

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

abstract = "The 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.",

author = "{The ATLAS collaboration} and G. Aad and B. Abbott and Abbott, {D. C.} and Abud, {A. Abed} and K. Abeling and Abhayasinghe, {D. K.} and Abidi, {S. H.} and AbouZeid, {O. S.} and Abraham, {N. L.} and H. Abramowicz and H. Abreu and Y. Abulaiti and Hoffman, {A. C.Abusleme} and Acharya, {B. S.} and B. Achkar and L. Adam and Bourdarios, {C. Adam} and L. Adamczyk and L. Adamek and J. Adelman and A. Adiguzel and S. Adorni and T. Adye and Affolder, {A. A.} and Y. Afik and C. Agapopoulou and Agaras, {M. N.} and A. Aggarwal and C. Agheorghiesei and Aguilar-Saavedra, {J. A.} and A. Ahmad and F. Ahmadov and Ahmed, {W. S.} and Anisenkov, {A. V.} and Baldin, {E. M.} and K. Beloborodov and Bobrovnikov, {V. S.} and Buzykaev, {A. R.} and Kazanin, {V. F.} and Kharlamov, {A. G.} and T. Kharlamova and Maslennikov, {A. L.} and Maximov, {D. A.} and Peleganchuk, {S. V.} and P. Podberezko and Rezanova, {O. L.} and Soukharev, {A. M.} and Talyshev, {A. A.} and Tikhonov, {Yu A.} and V. Zhulanov",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s).",

year = "2022",

month = jan,

doi = "10.1007/s41781-021-00062-2",

language = "English",

volume = "6",

journal = "Computing and Software for Big Science",

issn = "2510-2044",

publisher = "Springer Nature",

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 - The ATLAS collaboration

AU - Aad, G.

AU - Abbott, B.

AU - Abbott, D. C.

AU - Abud, A. Abed

AU - Abeling, K.

AU - Abhayasinghe, D. K.

AU - Abidi, S. H.

AU - AbouZeid, O. S.

AU - Abraham, N. L.

AU - Abramowicz, H.

AU - Abreu, H.

AU - Abulaiti, Y.

AU - Hoffman, A. C.Abusleme

AU - Acharya, B. S.

AU - Achkar, B.

AU - Adam, L.

AU - Bourdarios, C. Adam

AU - Adamczyk, L.

AU - Adamek, L.

AU - Adelman, J.

AU - Adiguzel, A.

AU - Adorni, S.

AU - Adye, T.

AU - Affolder, A. A.

AU - Afik, Y.

AU - Agapopoulou, C.

AU - Agaras, M. N.

AU - Aggarwal, A.

AU - Agheorghiesei, C.

AU - Aguilar-Saavedra, J. A.

AU - Ahmad, A.

AU - Ahmadov, F.

AU - Ahmed, W. S.

AU - Anisenkov, A. V.

AU - Baldin, E. M.

AU - Beloborodov, K.

AU - Bobrovnikov, V. S.

AU - Buzykaev, A. R.

AU - Kazanin, V. F.

AU - Kharlamov, A. G.

AU - Kharlamova, T.

AU - Maslennikov, A. L.

AU - Maximov, D. A.

AU - Peleganchuk, S. V.

AU - Podberezko, P.

AU - Rezanova, O. L.

AU - Soukharev, A. M.

AU - Talyshev, A. A.

AU - Tikhonov, Yu A.

AU - Zhulanov, V.

PY - 2022/1

Y1 - 2022/1

N2 - The 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 - The 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.

UR - http://www.scopus.com/inward/record.url?scp=85124276335&partnerID=8YFLogxK

U2 - 10.1007/s41781-021-00062-2

DO - 10.1007/s41781-021-00062-2

M3 - Article

AN - SCOPUS:85124276335

VL - 6

JO - Computing and Software for Big Science

JF - Computing and Software for Big Science

SN - 2510-2044

IS - 1

M1 - 3

ER -

ID: 35532936