Anamiati, G., Castillo-Felisola, O., Fonseca, R. M., Helo, J. C., & Hirsch, M. (2018). High-dimensional neutrino masses. J. High Energy Phys., 12(12), 066–26pp.
Abstract: For Majorana neutrino masses the lowest dimensional operator possible is the Weinberg operator at d = 5. Here we discuss the possibility that neutrino masses originate from higher dimensional operators. Specifically, we consider all tree-level decompositions of the d = 9, d = 11 and d = 13 neutrino mass operators. With renormalizable interactions only, we find 18 topologies and 66 diagrams for d = 9, and 92 topologies plus 504 diagrams at the d = 11 level. At d = 13 there are already 576 topologies and 4199 diagrams. However, among all these there are only very few genuine neutrino mass models: At d = (9, 11, 13) we find only (2,2,2) genuine diagrams and a total of (2,2,6) models. Here, a model is considered genuine at level d if it automatically forbids lower order neutrino masses without the use of additional symmetries. We also briefly discuss how neutrino masses and angles can be easily fitted in these high-dimensional models.
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Caron, S., Casas, J. A., Quilis, J., & Ruiz de Austri, R. (2018). Anomaly-free dark matter with harmless direct detection constraints. J. High Energy Phys., 12(12), 126–24pp.
Abstract: Dark matter (DM) interacting with the SM fields via a Z-boson (Z-portal') remains one of the most attractive WIMP scenarios, both from the theoretical and the phenomenological points of view. In order to avoid the strong constraints from direct detection and dilepton production, it is highly convenient that the Z has axial coupling to DM and leptophobic couplings to the SM particles, respectively. The latter implies that the associated U(1) coincides with baryon number in the SM sector. In this paper we completely classify the possible anomaly-free leptophobic Z with minimal dark sector, including the cases where the coupling to DM is axial. The resulting scenario is very predictive and perfectly viable from the present constraints from DM detection, EW observables and LHC data (di-lepton, di-jet and mono-jet production). We analyze all these constraints, obtaining the allowed areas in the parameter space, which generically prefer mZ less than or similar to 500 GeV, apart from resonant regions. The best chances to test these viable areas come from future LHC measurements.
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Mandal, R., & Pich, A. (2019). Constraints on scalar leptoquarks from lepton and kaon physics. J. High Energy Phys., 12(12), 089–40pp.
Abstract: We present a comprehensive analysis of low-energy signals of hypothetical scalar leptoquark interactions in lepton and kaon transitions. We derive the most general effective four-fermion Lagrangian induced by tree-level scalar leptoquark exchange and identify the Wilson coefficients predicted by the five possible types of scalar leptoquarks. The current constraints on the leptoquark Yukawa couplings arising from lepton and kaon processes are worked out, including also loop-induced transitions with only leptons (or quarks) as external states. In the presence of scalar leptoquark interactions, we also derive the differential distributions for flavour-changing neutral-current transitions in semileptonic kaon modes, including all known effects within the Standard Model. Their interference with the new physics contributions could play a significant role in future improvements of those constraints that are currently hampered by poorly-determined non-perturbative parameters.
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Escudero, M., Lopez-Pavon, J., Rius, N., & Sandner, S. (2020). Relaxing cosmological neutrino mass bounds with unstable neutrinos. J. High Energy Phys., 12(12), 119–44pp.
Abstract: At present, cosmological observations set the most stringent bound on the neutrino mass scale. Within the standard cosmological model (Lambda CDM), the Planck collaboration reports Sigma m(v)< 0.12 eV at 95 % CL. This bound, taken at face value, excludes many neutrino mass models. However, unstable neutrinos, with lifetimes shorter than the age of the universe <tau>(nu) less than or similar to t(U), represent a particle physics avenue to relax this constraint. Motivated by this fact, we present a taxonomy of neutrino decay modes, categorizing them in terms of particle content and final decay products. Taking into account the relevant phenomenological bounds, our analysis shows that 2-body decaying neutrinos into BSM particles are a promising option to relax cosmological neutrino mass bounds. We then build a simple extension of the type I seesaw scenario by adding one sterile state nu (4) and a Goldstone boson phi, in which nu (i)-> nu (4)phi decays can loosen the neutrino mass bounds up to Sigma m(v) similar to 1 eV, without spoiling the light neutrino mass generation mechanism. Remarkably, this is possible for a large range of the right-handed neutrino masses, from the electroweak up to the GUT scale. We successfully implement this idea in the context of minimal neutrino mass models based on a U(1)(mu-tau) flavor symmetry, which are otherwise in tension with the current bound on Sigma m(v).
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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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