A factorisation-aware Matrix element emulator
Abstract In this article we present a neural network based model to emulate matrix elements. This model improves on existing methods by taking advantage of the known factorisation properties of matrix elements. In doing so we can control the behaviour of simulated matrix elements when extrapolating...
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2021
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oai:doaj.org-article:586a39d7e38e4044bcd62be0948fce6c2021-11-14T12:40:55ZA factorisation-aware Matrix element emulator10.1007/JHEP11(2021)0661029-8479https://doaj.org/article/586a39d7e38e4044bcd62be0948fce6c2021-11-01T00:00:00Zhttps://doi.org/10.1007/JHEP11(2021)066https://doaj.org/toc/1029-8479Abstract In this article we present a neural network based model to emulate matrix elements. This model improves on existing methods by taking advantage of the known factorisation properties of matrix elements. In doing so we can control the behaviour of simulated matrix elements when extrapolating into more singular regions than the ones used for training the neural network. We apply our model to the case of leading-order jet production in e + e − collisions with up to five jets. Our results show that this model can reproduce the matrix elements with errors below the one-percent level on the phase-space covered during fitting and testing, and a robust extrapolation to the parts of the phase-space where the matrix elements are more singular than seen at the fitting stage.D. MaîtreH. TruongSpringerOpenarticlePerturbative QCDScattering AmplitudesNuclear and particle physics. Atomic energy. RadioactivityQC770-798ENJournal of High Energy Physics, Vol 2021, Iss 11, Pp 1-24 (2021) |
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Perturbative QCD Scattering Amplitudes Nuclear and particle physics. Atomic energy. Radioactivity QC770-798 |
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Perturbative QCD Scattering Amplitudes Nuclear and particle physics. Atomic energy. Radioactivity QC770-798 D. Maître H. Truong A factorisation-aware Matrix element emulator |
description |
Abstract In this article we present a neural network based model to emulate matrix elements. This model improves on existing methods by taking advantage of the known factorisation properties of matrix elements. In doing so we can control the behaviour of simulated matrix elements when extrapolating into more singular regions than the ones used for training the neural network. We apply our model to the case of leading-order jet production in e + e − collisions with up to five jets. Our results show that this model can reproduce the matrix elements with errors below the one-percent level on the phase-space covered during fitting and testing, and a robust extrapolation to the parts of the phase-space where the matrix elements are more singular than seen at the fitting stage. |
format |
article |
author |
D. Maître H. Truong |
author_facet |
D. Maître H. Truong |
author_sort |
D. Maître |
title |
A factorisation-aware Matrix element emulator |
title_short |
A factorisation-aware Matrix element emulator |
title_full |
A factorisation-aware Matrix element emulator |
title_fullStr |
A factorisation-aware Matrix element emulator |
title_full_unstemmed |
A factorisation-aware Matrix element emulator |
title_sort |
factorisation-aware matrix element emulator |
publisher |
SpringerOpen |
publishDate |
2021 |
url |
https://doaj.org/article/586a39d7e38e4044bcd62be0948fce6c |
work_keys_str_mv |
AT dmaitre afactorisationawarematrixelementemulator AT htruong afactorisationawarematrixelementemulator AT dmaitre factorisationawarematrixelementemulator AT htruong factorisationawarematrixelementemulator |
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1718429106818777088 |