{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/refinable-modeling-for-unbinned-smeft","title":"Refinable modeling for unbinned SMEFT analyses","arxiv_id":"2406.19076","date":"2024-06-27","proceeding":null,"authors":["Robert Schöfbeck"],"abstract":"We present techniques for estimating the effects of systematic uncertainties in unbinned data analyses at the LHC. Our primary focus is constraining the Wilson coefficients in the standard model effective field theory (SMEFT), but the methodology applies to broader parametric models of phenomena beyond the standard model (BSM). We elevate the well-established procedures for binned Poisson counting experiments to the unbinned case by utilizing machine-learned surrogates of the likelihood ratio. This approach can be applied to various theoretical, modeling, and experimental uncertainties. By establishing a common statistical framework for BSM and systematic effects, we lay the groundwork for future unbinned analyses at the LHC. Additionally, we introduce a novel tree-boosting algorithm capable of learning highly accurate parameterizations of systematic effects. This algorithm extends the existing toolkit with a versatile and robust alternative. We demonstrate our approach using the example of an SMEFT interpretation of highly energetic top quark pair production in proton-proton collisions.","url_abs":"https://arxiv.org/abs/2406.19076v3","url_pdf":"https://arxiv.org/pdf/2406.19076v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"refinable-modeling-for-unbinned-smeft","repo_url":"https://github.com/hephyanalysissw/bpt","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2406.19076","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.19076"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/hephyanalysissw/bpt","reach":null}],"summary":{"ran_honours":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"282345390f9b7443","entry":"parse_value","repo":"hephyanalysissw/bpt","repo_kind":"official","path":"bpt_training.py","file_url":"https://github.com/hephyanalysissw/bpt/blob/HEAD/bpt_training.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"282345390f9b7443"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}