Improved constraints on effective top quark interactions using edge convolution networks
Abstract We explore the potential of Graph Neural Networks (GNNs) to improve the performance of high-dimensional effective field theory parameter fits to collider data beyond traditional rectangular cut-based differential distribution analyses. In this study, we focus on a SMEFT analysis of pp → t t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-04-01
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| Series: | Journal of High Energy Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/JHEP04(2022)137 |
| _version_ | 1797865782276784128 |
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| author | Oliver Atkinson Akanksha Bhardwaj Stephen Brown Christoph Englert David J. Miller Panagiotis Stylianou |
| author_facet | Oliver Atkinson Akanksha Bhardwaj Stephen Brown Christoph Englert David J. Miller Panagiotis Stylianou |
| author_sort | Oliver Atkinson |
| collection | DOAJ |
| description | Abstract We explore the potential of Graph Neural Networks (GNNs) to improve the performance of high-dimensional effective field theory parameter fits to collider data beyond traditional rectangular cut-based differential distribution analyses. In this study, we focus on a SMEFT analysis of pp → t t ¯ $$ t\overline{t} $$ production, including top decays, where the linear effective field deformation is parametrised by thirteen independent Wilson coefficients. The application of GNNs allows us to condense the multidimensional phase space information available for the discrimination of BSM effects from the SM expectation by considering all available final state correlations directly. The number of contributing new physics couplings very quickly leads to statistical limitations when the GNN output is directly employed as an EFT discrimination tool. However, a selection based on minimising the SM contribution enhances the fit’s sensitivity when reflected as a (non-rectangular) selection on the inclusive data samples that are typically employed when looking for non-resonant deviations from the SM by means of differential distributions. |
| first_indexed | 2024-04-09T23:13:42Z |
| format | Article |
| id | doaj.art-05de6bdc53cc4a0188a9ce82169bebae |
| institution | Directory Open Access Journal |
| issn | 1029-8479 |
| language | English |
| last_indexed | 2024-04-09T23:13:42Z |
| publishDate | 2022-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of High Energy Physics |
| spelling | doaj.art-05de6bdc53cc4a0188a9ce82169bebae2023-03-22T10:11:23ZengSpringerOpenJournal of High Energy Physics1029-84792022-04-012022412010.1007/JHEP04(2022)137Improved constraints on effective top quark interactions using edge convolution networksOliver Atkinson0Akanksha Bhardwaj1Stephen Brown2Christoph Englert3David J. Miller4Panagiotis Stylianou5School of Physics & Astronomy, University of GlasgowSchool of Physics & Astronomy, University of GlasgowSchool of Physics & Astronomy, University of GlasgowSchool of Physics & Astronomy, University of GlasgowSchool of Physics & Astronomy, University of GlasgowSchool of Physics & Astronomy, University of GlasgowAbstract We explore the potential of Graph Neural Networks (GNNs) to improve the performance of high-dimensional effective field theory parameter fits to collider data beyond traditional rectangular cut-based differential distribution analyses. In this study, we focus on a SMEFT analysis of pp → t t ¯ $$ t\overline{t} $$ production, including top decays, where the linear effective field deformation is parametrised by thirteen independent Wilson coefficients. The application of GNNs allows us to condense the multidimensional phase space information available for the discrimination of BSM effects from the SM expectation by considering all available final state correlations directly. The number of contributing new physics couplings very quickly leads to statistical limitations when the GNN output is directly employed as an EFT discrimination tool. However, a selection based on minimising the SM contribution enhances the fit’s sensitivity when reflected as a (non-rectangular) selection on the inclusive data samples that are typically employed when looking for non-resonant deviations from the SM by means of differential distributions.https://doi.org/10.1007/JHEP04(2022)137Beyond Standard ModelEffective Field Theories |
| spellingShingle | Oliver Atkinson Akanksha Bhardwaj Stephen Brown Christoph Englert David J. Miller Panagiotis Stylianou Improved constraints on effective top quark interactions using edge convolution networks Journal of High Energy Physics Beyond Standard Model Effective Field Theories |
| title | Improved constraints on effective top quark interactions using edge convolution networks |
| title_full | Improved constraints on effective top quark interactions using edge convolution networks |
| title_fullStr | Improved constraints on effective top quark interactions using edge convolution networks |
| title_full_unstemmed | Improved constraints on effective top quark interactions using edge convolution networks |
| title_short | Improved constraints on effective top quark interactions using edge convolution networks |
| title_sort | improved constraints on effective top quark interactions using edge convolution networks |
| topic | Beyond Standard Model Effective Field Theories |
| url | https://doi.org/10.1007/JHEP04(2022)137 |
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