Simplified fast detector simulation in MadAnalysis 5

Abstract We introduce a new simplified fast detector simulator in the MadAnalysis 5 platform. The Python-like interpreter of the programme has been augmented by new commands allowing for a detector parametrisation through smearing and efficiency functions. On run time, an associated C++ code is auto...

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Main Authors: Jack Y. Araz, Benjamin Fuks, Georgios Polykratis
Format: Article
Language:English
Published: SpringerOpen 2021-04-01
Series:European Physical Journal C: Particles and Fields
Online Access:https://doi.org/10.1140/epjc/s10052-021-09052-5
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spelling doaj-e7c5779e91a84197b17ca8954d3f10292021-04-25T11:44:08ZengSpringerOpenEuropean Physical Journal C: Particles and Fields1434-60441434-60522021-04-0181412410.1140/epjc/s10052-021-09052-5Simplified fast detector simulation in MadAnalysis 5Jack Y. Araz0Benjamin Fuks1Georgios Polykratis2School of Physics & Astronomy, University of GlasgowLaboratoire de Physique Théorique et Hautes Energies (LPTHE), UMR 7589, Sorbonne Université et CNRSLaboratoire de Physique Théorique et Hautes Energies (LPTHE), UMR 7589, Sorbonne Université et CNRSAbstract We introduce a new simplified fast detector simulator in the MadAnalysis 5 platform. The Python-like interpreter of the programme has been augmented by new commands allowing for a detector parametrisation through smearing and efficiency functions. On run time, an associated C++ code is automatically generated and executed to produce reconstructed-level events. In addition, we have extended the MadAnalysis 5 recasting infrastructure to support our detector emulator, and we provide predefined LHC detector configurations. We have compared predictions obtained with our approach to those resulting from the usage of the Delphes 3 software, both for Standard Model processes and a few new physics signals. Results generally agree to a level of about 10% or better, the largest differences in the predictions stemming from the different strategies that are followed to model specific detector effects. Equipped with these new functionalities, MadAnalysis 5 now offers a new user-friendly way to include detector effects when analysing collider events, the simulation of the detector and the analysis being both handled either through a set of intuitive Python commands or directly within the C++ core of the platform.https://doi.org/10.1140/epjc/s10052-021-09052-5
collection DOAJ
language English
format Article
sources DOAJ
author Jack Y. Araz
Benjamin Fuks
Georgios Polykratis
spellingShingle Jack Y. Araz
Benjamin Fuks
Georgios Polykratis
Simplified fast detector simulation in MadAnalysis 5
European Physical Journal C: Particles and Fields
author_facet Jack Y. Araz
Benjamin Fuks
Georgios Polykratis
author_sort Jack Y. Araz
title Simplified fast detector simulation in MadAnalysis 5
title_short Simplified fast detector simulation in MadAnalysis 5
title_full Simplified fast detector simulation in MadAnalysis 5
title_fullStr Simplified fast detector simulation in MadAnalysis 5
title_full_unstemmed Simplified fast detector simulation in MadAnalysis 5
title_sort simplified fast detector simulation in madanalysis 5
publisher SpringerOpen
series European Physical Journal C: Particles and Fields
issn 1434-6044
1434-6052
publishDate 2021-04-01
description Abstract We introduce a new simplified fast detector simulator in the MadAnalysis 5 platform. The Python-like interpreter of the programme has been augmented by new commands allowing for a detector parametrisation through smearing and efficiency functions. On run time, an associated C++ code is automatically generated and executed to produce reconstructed-level events. In addition, we have extended the MadAnalysis 5 recasting infrastructure to support our detector emulator, and we provide predefined LHC detector configurations. We have compared predictions obtained with our approach to those resulting from the usage of the Delphes 3 software, both for Standard Model processes and a few new physics signals. Results generally agree to a level of about 10% or better, the largest differences in the predictions stemming from the different strategies that are followed to model specific detector effects. Equipped with these new functionalities, MadAnalysis 5 now offers a new user-friendly way to include detector effects when analysing collider events, the simulation of the detector and the analysis being both handled either through a set of intuitive Python commands or directly within the C++ core of the platform.
url https://doi.org/10.1140/epjc/s10052-021-09052-5
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AT benjaminfuks simplifiedfastdetectorsimulationinmadanalysis5
AT georgiospolykratis simplifiedfastdetectorsimulationinmadanalysis5
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