相关论文: PhyLiNO: A Forward-Folding Likelihood-Fit Framewor…

An extension of the New Standard Model, by introducing a mixing of the low mass ``active'' neutrinos with heavy ones, or by any model with lepton flavor violation, is considered. This leads to non-orthogonal neutrino production and…

We present a three-parameter neutrino-oscillation model for three flavors of massless neutrinos with Fermi-point splitting and tri-maximal mixing angles. One of these parameters is the T-violating phase \epsilon, for which the experimental…

A substantial fraction of systematic uncertainties in neutrino oscillation experiments stem from the lack of precision in modeling the nuclear target in neutrino-nucleus interactions. Whilst this has driven significant progress in the…

Neutrino oscillations are at the forefront of advances in Physics beyond the Standard Model. Increasing accuracy in measurements of the neutrino mixing matrix is an important challenge in current experiments. It depends on parameters that…

Neutrino oscillations encode fundamental information about neutrino masses and mixing parameters, offering a unique window into physics beyond the Standard Model. Estimating these parameters from oscillation probability maps is, however,…

The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose experiment under construction in southern China. JUNO aims to determine the neutrino mass ordering and precisely measure the neutrino oscillation parameters by…

Neutrino physics offers unique insights into phenomena beyond the Standard Model (BSM). This thesis presents phenomenological investigations organized around three pillars: consolidation of the three-flavor oscillation paradigm, exploration…

We present a global analysis of neutrino oscillation data within the three-neutrino oscillation scheme, including in our fit all the current solar neutrino data, the reactor neutrino data from KamLAND and CHOOZ, the atmospheric neutrino…

In machine learning, likelihood-free inference refers to the task of performing an analysis driven by data instead of an analytical expression. We discuss the application of Neural Spline Flows, a neural density estimation algorithm, to the…

We present a computer framework to store and evaluate likelihoods coming from High Energy Physics experiments. Due to its flexibility it can be interfaced with existing fitting codes and allows to uniform the interpretation of the…

Beam neutrino oscillation experiments typically employ only one detector at a certain baseline, apart from the near detector that measures the unoscillated neutrino flux at the source. Lately, there have been discussions of having detectors…

We study to what extent upcoming precision neutrino oscillation experiments will be able to exclude one of the most predictive models of neutrino mass and mixing: the Littlest Seesaw. We show that this model provides a good fit to current…

Recently, we suggested that low-energy beta-beam neutrinos can be very useful for the study of supernova neutrino interactions. In this paper, we examine the use of a such experiment for the analysis of a supernova neutrino signal. Since…

Precise neutrino energy reconstruction is essential for next-generation long-baseline oscillation experiments, yet current methods remain limited by large uncertainties in neutrino-nucleus interaction modeling. Even so, it is well…

Future neutrino oscillation experiments will lead to precision measurements of neutrino mass splittings and mixings. The flavour structure of the lepton sector will therefore at some point become better known than that of the quark sector.…

New data on neutrino masses is very exciting. It is the first evidence for physics beyond the standard model. In this talk we discuss this data and how it may (or may not) be used to constrain theories of charged fermion masses. The talk is…

Understanding nuclear effects is essential for improving the sensitivity of neutrino oscillation measurements. Validating nuclear models solely through neutrino scattering data is challenging due to limited statistics and the broad energy…

We analyze the neutrino mass matrix entries and their correlations in a probabilistic fashion, constructing probability distribution functions using the latest results from neutrino oscillation fits. Two cases are considered: the standard…

We study the oscillations of neutrinos in a model in which the neutrino is coupled to a localized, idealized source and detector. By varying the spatial and temporal resolution of the source and detector we are able to model the full range…

Designing deep learning-based solutions is becoming a race for training deeper models with a greater number of layers. While a large-size deeper model could provide competitive accuracy, it creates a lot of logistical challenges and…