Abstract: The interpretation of present and future neutrino experiments requires accurate theoretical predictions for neutrino-nucleus scattering rates. Neutrino structure functions can be reliably evaluated in the deep-inelastic scattering regime within the perturbative QCD (pQCD) framework. At low momentum transfers (Q(2) less than or similar to few GeV2), inelastic structure functions are however affected by large uncertainties which distort event rate predictions for neutrino energies E-nu up to the TeV scale. Here we present a determination of neutrino inelastic structure functions valid for the complete range of energies relevant for phenomenology, from the GeV region entering oscillation analyses to the multi-EeV region accessible at neutrino telescopes. Our NNSF nu approach combines a machine-learning parametrisation of experimental data with pQCD calculations based on state-of-the-art analyses of proton and nuclear parton distributions (PDFs). We compare our determination to other calculations, in particular to the popular Bodek-Yang model. We provide updated predictions for inclusive cross sections for a range of energies and target nuclei, including those relevant for LHC far-forward neutrino experiments such as FASER nu, SND@LHC, and the Forward Physics Facility. The NNSF nu determination is made available as fast interpolation LHAPDF grids, and it can be accessed both through an independent driver code and directly interfaced to neutrino event generators such as GENIE.

Abstract: A Standard Model extension with two Majorana neutrinos can explain the measured neutrino masses and mixings, and also account for the matter-antimatter asymmetry in a region of parameter space that could be testable in future experiments. The testability of the model relies to some extent on its minimality. In this paper we address the possibility that the model might be extended by extra generic new physics which we parametrize in terms of a low-energy effective theory. We consider the effects of the operators of the lowest dimensionality, d = 5, and evaluate the upper bounds on the coefficients so that the predictions of the minimal model are robust. One of the operators gives a new production mechanism for the heavy neutrinos at LHC via higgs decays. The higgs can decay to a pair of such neutrinos that, being long-lived, leave a powerful signal of two displaced vertices. We estimate the LHC reach to this process.

Abstract: We propose a Pati-Salam extension of the standard model incorporating a flavor symmetry based on the Delta (27) group. The theory realizes a realistic Froggatt-Nielsen picture of quark mixing and a predictive pattern of neutrino oscillations. We find that, for normal neutrino mass ordering, the atmospheric angle must lie in the higher octant, CP must be violated in oscillations, and there is a lower bound for the 0 nu beta beta decay rate. For the case of inverted mass ordering, we find that the lower atmospheric octant is preferred, and that CP can be conserved in oscillations. Neutrino masses arise from a low-scale seesaw mechanism, whose messengers can be produced by a Z' portal at the LHC.

Abstract: We propose a minimal model where a dark sector seeds neutrino mass generation radiatively within the linear seesaw mechanism. Neutrino masses are calculable, since treelevel contributions are forbidden by symmetry. They arise from spontaneous lepton number violation by a small Higgs triplet vacuum expectation value. Lepton flavour violating processes e.g. μ-> e gamma can be sizeable, despite the tiny neutrino masses. We comment also on dark-matter and collider implications.

Abstract: Model-independent search strategies have been increasingly proposed in recent years because on the one hand there has been no clear signal for new physics and on the other hand there is a lack of a highly probable and parameter-free extension of the standard model. For these reasons, there is no simple search target so far. In this work, we try to take a new direction and ask the question: bearing in mind that we have a large number of new physics theories that go beyond the Standard Model and may contain a grain of truth, can we improve our search strategy for unknown signals by using them “in combination”? In particular, we show that a signal hypothesis based on a large, intermingled set of many different theoretical signal models can be a superior approach to find an unknown BSM signal. Applied to a recent data challenge, we show that “mixture-of-theories training” outperforms strategies that optimize signal regions with a single BSM model as well as most unsupervised strategies. Applications of this work include anomaly detection and the definition of signal regions in the search for signals of new physics.