相关论文: On the Bayesian approach to neutrino mass ordering

Bayesian optimization is a class of global optimization techniques. In Bayesian optimization, the underlying objective function is modeled as a realization of a Gaussian process. Although the Gaussian process assumption implies a random…

Past and current direct neutrino mass experiments set limits on the so-called effective neutrino mass, which is an incoherent sum of neutrino masses and lepton mixing matrix elements. The electron energy spectrum which neglects the…

Context: Astronomy and astrophysics demand rigorous handling of uncertainties to ensure the credibility of outcomes. The growing integration of artificial intelligence offers a novel avenue to address this necessity. This convergence…

Understanding the uncertainty of a neural network's (NN) predictions is essential for many purposes. The Bayesian framework provides a principled approach to this, however applying it to NNs is challenging due to large numbers of parameters…

We present an overview of the current status of neutrino oscillation studies at atmospheric neutrino experiments. While the current data gives some tentalising hints regarding the neutrino mass hierarchy, octant of $\theta_{23}$ and…

Emergence of neutrino physics is fueled by the recent growth in quality and quantity of experimental data, yet, there are still open questions. How much can the determination of mixing parameters be improved? Can we improve bounds on…

This paper introduces and reviews some of the principles and methods used in Bayesian reliability. It specifically discusses methods used in the analysis of success/no-success data and then reminds the reader of a simple Monte Carlo…

After a brief introduction to neutrino mass via the see-saw model I discuss neutrino mixing and oscillation, first in vacuum and then its matter enhancement. Then the solar and atmospheric neutrino oscillation data are briefly reviewed.…

We analyze a modular invariant model of lepton masses, with neutrino masses originating either from the Weinberg operator or from the seesaw. The constraint provided by modular invariance is so strong that neutrino mass ratios, lepton…

Physics potential of future measurements of atmospheric neutrinos is explored. Observation of $\Delta m^2_{21}$ driven sub-dominant effects and $\theta_{13}$ driven large matter effects in atmospheric neutrinos can be used to study the…

Bayesian nonparametric models offer a flexible and powerful framework for statistical model selection, enabling the adaptation of model complexity to the intricacies of diverse datasets. This survey intends to delve into the significance of…

In this paper the elicitation of probabilities from human experts is considered as a measurement process, which may be disturbed by random 'measurement noise'. Using Bayesian concepts a second order probability distribution is derived…

Matter at ultra-high densities finds a physical realization inside neutron stars. One key property is their maximum mass, which has far-reaching implications for astrophysics and the equation of state of ultra dense matter. In this work, we…

This paper offers a qualitative insight into the convergence of Bayesian parameter inference in a setup which mimics the modeling of the spread of a disease with associated disease measurements. Specifically, we are interested in the…

We discuss a methodology of machine learning to deduce the neutron star equation of state from a set of mass-radius observational data. We propose an efficient procedure to deal with a mapping from finite data points with observational…

We argue that the evidence for neutrino mass is quite compelling. This mass raises a number of questions, which we enumerate, about neutrinos. Then we focus on one of these questions---the issue of the possible neutrino mass spectra. In…

With the compelling evidence for massive neutrinos from recent neutrino oscillation experiments, one of the most fundamental tasks of particle physics over the next years will be the determination of the absolute mass scale of neutrinos,…

Pulsar-timing datasets have been analyzed with great success using probabilistic treatments based on Gaussian distributions, with applications ranging from studies of neutron-star structure to tests of general relativity and searches for…

The distribution of masses for neutron stars is analyzed using the Bayesian statistical inference, evaluating the likelihood of proposed gaussian peaks by using fifty-four measured points obtained in a variety of systems. The results…

Nearly 70 years since the neutrino was discovered, and 25 years since discovery of neutrino oscillations established its non-zero mass, the absolute neutrino-mass scale remains unknown. Due to its unique characteristics, determining this…