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Classifying standard model extensions effectively with precision observables

Das Bakshi, Supratim and Chakrabortty, Joydeep and Spannowsky, Michael (2021) 'Classifying standard model extensions effectively with precision observables.', Physical Review D, 103 (5).

Abstract

Effective theories are well established theoretical frameworks to describe the effect of energetically widely separated UV models on observables at lower energy scales. Due to the complexity of the effective theory when taking all the Standard Model symmetries and degrees of freedom into account, tensioning the entire system in a completely agnostic way against experimental measurements results in constraints on the Wilson coefficients of the effective operators that either bears little information or challenge intrinsic assumptions imposed on the effective field theory framework. In general, a specific high-scale extension of the Standard Model only induces a subset of all possible operators. Thus, by investigating which operators are induced by different classes of the Standard Model extensions and comparing to which precision observables they contribute, we show that it is possible to obtain an improved understanding of which UV model is realised in nature. We present the tree + 1 -loop matching results for dimension-6 operators of 15 different BSM scenarios onto SMEFT, and also including, the specific model-based contributions to the observables. We argue that more observables and matching with higher theoretical precision will pave the way to distinguish the single scalar extensions of the SM signatures uniquely. We promote this approach to study new sets of observables in the context of current and near future experiments.

Item Type:Article
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Available under License - Creative Commons Attribution 4.0.
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1103/PhysRevD.103.056019
Publisher statement:Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.
Date accepted:01 March 2021
Date deposited:13 September 2021
Date of first online publication:23 March 2021
Date first made open access:13 September 2021

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