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April 14, 2023Computer Physics Communications59 citationsOpen Access

HighPT: A tool for high-p Drell-Yan tails beyond the standard model

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LALukas AllwicherDFDarius A. FaroughyFJFlorentin Jaffredo

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Abstract

HighPT is a Mathematica package for the analysis of high-energy data of semileptonic transitions at hadron colliders. It allows to compute high-pT tail observables for semileptonic processes, i.e. Drell-Yan cross sections, for dilepton and monolepton final states at the LHC. These observables can be calculated at tree level within the Standard Model Effective Field Theory, including the relevant operators up to dimension eight to ensure a consistent description of the cross section including terms of O(Λ−4) in the cutoff scale Λ. For New Physics models with new mediators that can be resolved at LHC energies, HighPT can also account for the full propagation effects of these new bosonic states at tree level. Using the available data from the high-pT tails in the relevant LHC run-II searches by the ATLAS and CMS collaborations, HighPT can also construct the corresponding likelihoods for all possible flavors of the leptonic final states. As an illustration, we derive and compare constraints on Wilson coefficients at different orders in the Effective Field Theory expansion, and we investigate lepton flavor violation for the S3 leptoquark model. The HighPT code is publicly available at . Program title: HighPT CPC Library link to program files: https://doi.org/10.17632/zh5yx6j4xj.1 Developer's repository link: https://github.com/HighPT/HighPT Licensing provisions: MIT license Programming language: Mathematica Nature of problem: Constraining New Physics scenarios with high-pT Drell-Yan data. Solution method: We present the first tool allowing to extract bounds, computed at leading order, on semileptonic transitions in the Standard Model Effective Field Theory, including operators up to dimension eight, and in explicit New Physics models.

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Allwicher et al. (2023) studied this question.

synapsesocial.com/papers/6a634805a98bc91ae4c1a79dhttps://doi.org/10.1016/j.cpc.2023.108749
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