Abstract: The atmospheric neutrino passing fraction, or self-veto, is defined as the probability for an atmospheric neutrino not to be accompanied by a detectable muon from the same cosmic-ray air shower. Building upon previous work, we propose a redefinition of the passing fractions by unifying the treatment for muon and electron neutrinos. Several approximations have also been removed. This enables performing detailed estimations of the uncertainties in the passing fractions from several inputs: muon losses, cosmic-ray spectrum, hadronic-interaction models and atmosphere-density profiles. We also study the passing fractions under variations of the detector configuration: depth, surrounding medium and muon veto trigger probability. The calculation exhibits excellent agreement with passing fractions obtained from Monte Carlo simulations. Finally, we provide a general software framework to implement this veto technique for all large-scale neutrino observatories.

Abstract: Theoretical interpretations of particle physics data, such as the determination of the Wilson coefficients of the Standard Model Effective Field Theory (SMEFT), often involve the inference of multiple parameters from a global dataset. Optimizing such interpretations requires the identification of observables that exhibit the highest possible sensitivity to the underlying theory parameters. In this work we develop a flexible open source frame-work, ML4EFT, enabling the integration of unbinned multivariate observables into global SMEFT fits. As compared to traditional measurements, such observables enhance the sensitivity to the theory parameters by preventing the information loss incurred when binning in a subset of final-state kinematic variables. Our strategy combines machine learning regression and classification techniques to parameterize high-dimensional likelihood ratios, using the Monte Carlo replica method to estimate and propagate methodological uncertainties. As a proof of concept we construct unbinned multivariate observables for top-quark pair and Higgs+Z production at the LHC, demonstrate their impact on the SMEFT parameter space as compared to binned measurements, and study the improved constraints associated to multivariate inputs. Since the number of neural networks to be trained scales quadratically with the number of parameters and can be fully parallelized, the ML4EFT framework is well-suited to construct unbinned multivariate observables which depend on up to tens of EFT coefficients, as required in global fits.

Abstract: The Scotogenic model is a popular scenario that induces radiative Majorana neutrino masses and includes a weakly-interacting dark matter candidate. We classify all possible ultraviolet extensions of the Scotogenic model in which (i) the dark DOUBLE-STRUCK CAPITAL Z(2) parity emerges at low energies after the spontaneous breaking of a global U(1)(L) lepton number symmetry, and (ii) the low-energy effective theory contains a naturally small lepton number breaking parameter, suppressed by the mass of a heavy mediator integrated out at tree-level. We find 50 such models and discuss two of them in detail to illustrate our setup. We also discuss some general aspects of the phenomenology of the models in our classification, exploring possible lepton flavor violating signals, collider signatures and implications for dark matter. The phenomenological prospects of these scenarios are very rich due to the presence of additional scalar states, including a massless Goldstone boson.