Cirigliano, V., Jenkins, J. P., & Gonzalez-Alonso, M. (2010). Semileptonic decays of light quarks beyond the Standard Model. Nucl. Phys. B, 830(1-2), 95–115.
Abstract: We describe non-standard contributions to semileptonic processes in a model independent way in terms of in SU(2)(L) x U(1)(Y) invariant effective lagrangian at the weak scale, front which we derive the low-energy effective lagrangian governing muon and beta decays. We find that the deviation from Cabibbo universality, Delta(CKM) equivalent to vertical bar V-ud vertical bar(2) + vertical bar V-us vertical bar(2) + vertical bar V-ub vertical bar(2) – 1, receives contributions from four effective operators. The phenomenological bound Delta(CKM) = (-1 +/- 6) x 10(-4) provides strong constraints on all four operators, corresponding to art effective scale Lambda > 11 TeV (90% CL). Depending on the operator, this constraint is at the same level or better then the Z pole observables. Conversely, precision electroweak constraints alone would allow universality violations as large as Delta(CKM) = -0.01 (90% CL). An observed Delta(CKM) not equal 0 at this level Could be explained in terms of a single four-fermion operator which is relatively poorly constrained by electroweak precision measurements.
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Sanchis-Lozano, M. A., & Sarkisyan-Grinbaum, E. K. (2018). Searching for new physics with three-particle correlations in pp collisions at the LHC. Phys. Lett. B, 781, 505–509.
Abstract: New phenomena involving pseudorapidity and azimuthal correlations among final-state particles in pp collisions at the LHC can hint at the existence of hidden sectors beyond the Standard Model. In this paper we rely on a correlated-cluster picture of multiparticle production, which was shown to account for the ridge effect, to assess the effect of a hidden sector on three-particle correlations concluding that there is a potential signature of new physics that can be directly tested by experiments using well-known techniques.
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Sanchis-Lozano, M. A., Sarkisyan-Grinbaum, E. K., & Moreno-Picot, S. (2016). Searching for hidden sector in multiparticle production at LHC. Phys. Lett. B, 754, 353–359.
Abstract: We study the impact of a hidden sector beyond the Standard Model, e.g. a Hidden Valley model, on factorial moments and cumulants of multiplicity distributions in multiparticle production with a special emphasis on the prospects for LHC results.
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Aebischer, J., Brivio, I., Celis, A., Evans, J. A., Jiang, Y., Kumar, J., et al. (2018). WCxf : An exchange format for Wilson coefficients beyond the Standard Model. Comput. Phys. Commun., 232, 71–83.
Abstract: We define a data exchange format for numerical values of Wilson coefficients of local operators parameterising low-energy effects of physics beyond the Standard Model. The format facilitates interfacing model-specific Wilson coefficient calculators, renormalisation group (RG) runners, and observable calculators. It is designed to be unambiguous (defining a non-redundant set of operators with fixed normalisation in each basis), extensible (allowing the addition of new EFTs or bases by the user), and robust (being based on industry standard file formats with parsers implemented in many programming languages). We have implemented the format for the Standard Model EFT (SMEFT) and for the weak effective theory (WET) below the electroweak scale and have added interfaces to a number of public codes dealing with SMEFT or WET. We also provide command-line utilities and a Python module for convenient manipulation of WCxf files, including translation between different bases and matching from SMEFT to WET. (C) 2018 Elsevier B.V. All rights reserved.
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Kasieczka, G. et al, & Sanz, V. (2021). The LHC Olympics 2020: a community challenge for anomaly detection in high energy physics. Rep. Prog. Phys., 84(12), 124201–64pp.
Abstract: A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.
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