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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Series: | European Physical Journal C: Particles and Fields |
Online Access: | https://doi.org/10.1140/epjc/s10052-021-09052-5 |
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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 |
work_keys_str_mv |
AT jackyaraz simplifiedfastdetectorsimulationinmadanalysis5 AT benjaminfuks simplifiedfastdetectorsimulationinmadanalysis5 AT georgiospolykratis simplifiedfastdetectorsimulationinmadanalysis5 |
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