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

We use neutron star mass and radius measurements to constrain the spontaneous scalarization phenomenon in scalar-tensor theories using Bayesian analysis. Neutron star structures in this scenario can be significantly different from the case…

The discovery of neutrino mass establishes the need for physics beyond the Standard Model. I summarize the status of two- and three-neutrino oscillation parameters from current solar, atmospheric, reactor and accelerator data. Future…

The Bayesian approach to the prediction of particle type given measurements of particle location is explored, using a parametric model whose prior is based on the transformation group. Two types of particle are considered, and locations are…

The preference of the normal neutrino mass ordering from the recent cosmological constraint and the global fit of neutrino oscillation experiments does not seem like a wise choice at first glance since it obscures the neutrinoless double…

In this review we present the main features of the current status of neutrino physics. After a review of the theory of neutrino mixing and oscillations, we discuss the current status of solar and atmospheric neutrino oscillation…

Neutrinos being massive could undergo non-radiative decay, a property for which the diffuse supernova neutrino background has a unique sensitivity. We extend previous analyses to explore our ability to disentangle predictions for the…

The ranking problem is to order a collection of units by some unobserved parameter, based on observations from the associated distribution. This problem arises naturally in a number of contexts, such as business, where we may want to rank…

The equation of state (EoS) of the neutron star (NS) matter remains an enigma. In this work we perform the Bayesian parameter inference with the gravitational wave data (GW170817) and mass-radius observations of some NSs (PSR J0030+0451,…

Bayesian quantum estimation provides a robust framework for quantum technologies, especially in scenarios with limited data and minimal prior information. Yet, its application to continuous-variable Gaussian systems has remained limited and…

Bayesian optimization has proven to be a highly effective methodology for the global optimization of unknown, expensive and multimodal functions. The ability to accurately model distributions over functions is critical to the effectiveness…

Whether the neutrino mass ordering is normal or inverted remains an experimentally open issue in neutrino physics. The knowledge of neutrino mass ordering has great importance for neutrinoless double-beta ($ 0\nu\beta\beta$) decay…

Neutrino oscillation and tritium beta decay experiments taken simultaneously into account are able to access the so far imperceptible absolute neutrino masses at the electronvolt level. The neutrino mass spectrum derived in this way is…

We explore the notion of uncertainty in the context of modern abstractive summarization models, using the tools of Bayesian Deep Learning. Our approach approximates Bayesian inference by first extending state-of-the-art summarization models…

I discuss neutrino mixing ansatze, such as the generalized Tri-bimaximal and bi-large mixing patterns, and their utility in describing the oscillation data. Unitarity tests and probes of the absolute neutrino mass scale are briefly…

Bayesian model mixing (BMM) is a statistical technique that can combine constraints from different regions of an input space in a principled way. Here we extend our BMM framework for the equation of state (EOS) of strongly interacting…

Randomized experiments are the gold standard for evaluating the effects of changes to real-world systems. Data in these tests may be difficult to collect and outcomes may have high variance, resulting in potentially large measurement error.…

Several recent works encourage the use of a Bayesian framework when assessing performance and fairness metrics of a classification algorithm in a supervised setting. We propose the Uncertainty Matters (UM) framework that generalizes a…

This paper explores an approach to Bayesian sample size determination in clinical trials. The approach falls into the category of what is often called "proper Bayesian", in that it does not mix frequentist concepts with Bayesian ones. A…

In this work we develop and implement a novel Bayesian method for computing the DOS of a system. This method is based on the use of a test function with adjustable parameters and we use Bayes theorem to find the best parameters given a…

Spectral estimation (SE) aims to identify how the energy of a signal (e.g., a time series) is distributed across different frequencies. This can become particularly challenging when only partial and noisy observations of the signal are…