Refinable modeling for unbinned SMEFT analyses
We present methods to estimate systematic uncertainties in unbinned large hadron collider (LHC) data analyses, focusing on constraining Wilson coefficients in the standard model effective field theory (SMEFT). Our approach also applies to broader parametric models of non-resonant phenomena beyond th...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/ad9fd1 |
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| author | Robert Schöfbeck |
| author_facet | Robert Schöfbeck |
| author_sort | Robert Schöfbeck |
| collection | DOAJ |
| description | We present methods to estimate systematic uncertainties in unbinned large hadron collider (LHC) data analyses, focusing on constraining Wilson coefficients in the standard model effective field theory (SMEFT). Our approach also applies to broader parametric models of non-resonant phenomena beyond the standard model. By using machine-learned surrogates of the likelihood ratio, we extend well-established procedures from binned Poisson counting experiments to the unbinned case. This framework handles various theoretical, modeling, and experimental uncertainties, laying the foundation for future unbinned analyses at the LHC. We also introduce a tree-boosting algorithm that learns precise parametrizations of systematic effects, providing a robust, flexible alternative to neural networks for modeling systematics. We demonstrate this approach with an SMEFT analysis of highly energetic top quark pair production in proton–proton collisions. |
| format | Article |
| id | doaj-art-a86b6d1800004c6faf5b44d42b09bf6c |
| institution | Kabale University |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-a86b6d1800004c6faf5b44d42b09bf6c2025-01-13T06:47:10ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016101500710.1088/2632-2153/ad9fd1Refinable modeling for unbinned SMEFT analysesRobert Schöfbeck0https://orcid.org/0000-0002-2332-8784Institute for High Energy Physics , Austrian Academy of Sciences, Dominikanerbastei 16, Vienna 1010, AustriaWe present methods to estimate systematic uncertainties in unbinned large hadron collider (LHC) data analyses, focusing on constraining Wilson coefficients in the standard model effective field theory (SMEFT). Our approach also applies to broader parametric models of non-resonant phenomena beyond the standard model. By using machine-learned surrogates of the likelihood ratio, we extend well-established procedures from binned Poisson counting experiments to the unbinned case. This framework handles various theoretical, modeling, and experimental uncertainties, laying the foundation for future unbinned analyses at the LHC. We also introduce a tree-boosting algorithm that learns precise parametrizations of systematic effects, providing a robust, flexible alternative to neural networks for modeling systematics. We demonstrate this approach with an SMEFT analysis of highly energetic top quark pair production in proton–proton collisions.https://doi.org/10.1088/2632-2153/ad9fd1systematic uncertaintieseffective field theorytree algorithmsboosting |
| spellingShingle | Robert Schöfbeck Refinable modeling for unbinned SMEFT analyses Machine Learning: Science and Technology systematic uncertainties effective field theory tree algorithms boosting |
| title | Refinable modeling for unbinned SMEFT analyses |
| title_full | Refinable modeling for unbinned SMEFT analyses |
| title_fullStr | Refinable modeling for unbinned SMEFT analyses |
| title_full_unstemmed | Refinable modeling for unbinned SMEFT analyses |
| title_short | Refinable modeling for unbinned SMEFT analyses |
| title_sort | refinable modeling for unbinned smeft analyses |
| topic | systematic uncertainties effective field theory tree algorithms boosting |
| url | https://doi.org/10.1088/2632-2153/ad9fd1 |
| work_keys_str_mv | AT robertschofbeck refinablemodelingforunbinnedsmeftanalyses |