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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2021-11-01
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| Series: | Journal of High Energy Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/JHEP11(2021)066 |
| _version_ | 1819006365152051200 |
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| author | D. Maître H. Truong |
| author_facet | D. Maître H. Truong |
| author_sort | D. Maître |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-21T00:07:31Z |
| format | Article |
| id | doaj.art-586a39d7e38e4044bcd62be0948fce6c |
| institution | Directory Open Access Journal |
| issn | 1029-8479 |
| language | English |
| last_indexed | 2024-12-21T00:07:31Z |
| publishDate | 2021-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of High Energy Physics |
| spelling | doaj.art-586a39d7e38e4044bcd62be0948fce6c2022-12-21T19:22:26ZengSpringerOpenJournal of High Energy Physics1029-84792021-11-0120211112410.1007/JHEP11(2021)066A factorisation-aware Matrix element emulatorD. Maître0H. Truong1Institute for Particle Physics Phenomenology, Durham UniversityInstitute for Particle Physics Phenomenology, Durham UniversityAbstract 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.https://doi.org/10.1007/JHEP11(2021)066Perturbative QCDScattering Amplitudes |
| spellingShingle | D. Maître H. Truong A factorisation-aware Matrix element emulator Journal of High Energy Physics Perturbative QCD Scattering Amplitudes |
| title | 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_short | A factorisation-aware Matrix element emulator |
| title_sort | factorisation aware matrix element emulator |
| topic | Perturbative QCD Scattering Amplitudes |
| url | https://doi.org/10.1007/JHEP11(2021)066 |
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