Related papers: Neutrino Oscillation Parameter Estimation Using St…
We formulate an alternative approach based on unitarity triangles to describe neutrino oscillations in presence of non-standard interactions (NSI). Using perturbation theory, we derive the expression for the oscillation probability in case…
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…
The NOW2000 Workshop summarized the present status and future possibilities for all aspects of neutrino physics and astrophysics. Neutrino oscillation physics has truly come of age in the last few years. It is now data driven (analogous to…
It is shown that neutrino oscillation processes can be consistently described in the framework of quantum field theory. Namely, the oscillating electron survival probabilities in experiments with neutrino detection by charged-current and…
The simplest unified extension of the MSSM with bi-linear R--Parity violation naturally predicts a hierarchical neutrino mass spectrum, in which one neutrino acquires mass by mixing with neutralinos, while the other two get mass…
Existing oscillation data point to nonzero neutrino masses with large mixings. We analyze the generic features of the neutrino Majorana mass matrix with inverted hierarchy and construct realistic {\it minimal schemes} for the neutrino mass…
Simulation of high-order nonlinear system requires extensive computational resources, especially in modern VLSI backend design where bifurcation-induced instability and chaos-like transient behaviors pose challenges. We present S-Crescendo…
Complex computer simulations are commonly required for accurate data modelling in many scientific disciplines, making statistical inference challenging due to the intractability of the likelihood evaluation for the observed data.…
Large mass ice/water Cherenkov experiments, optimized to detect low energy (1-20 GeV) atmospheric neutrinos, have the potential to discriminate between normal and inverted neutrino mass hierarchies. The sensitivity depends on several model…
We study the effects of neutrino oscillation on supernova neutrino in the case of the inverted mass hierarchy ($m_{3} \ll m_{1} < m_{2}$) as well as the normal mass hierarchy ($m_{1} < m_{2} \ll m_{3}$). Numerical analysis using a realistic…
Single crystal inelastic neutron scattering data contain rich information about the structure and dynamics of a material. Yet the challenge of matching sophisticated theoretical models with large data volumes is compounded by computational…
Phenomenology of neutrino oscillations in vacuum and in cosmological plasma is considered. Neutrino oscillations in vacuum are usually described in plane wave approximation. In this formalism there is an ambiguity if one should assume…
We present results of a combined analysis in neutrino oscillations without unitarity assumption in the $3\nu$ mixing picture. Constraints on neutrino mixing matrix elements are based on recent data from the reactor, solar and long-baseline…
The possibility to determine the type of neutrino mass hierarchy by studying atmospheric neutrino oscillations with a detector capable to distinguish between neutrino and antineutrino events, such as magnetized iron calorimeters, is…
The application of neural network models to scientific machine learning tasks has proliferated in recent years. In particular, neural network models have proved to be adept at modeling processes with spatial-temporal complexity.…
An analysis of atmospheric neutrino data from all four run periods of \superk optimized for sensitivity to the neutrino mass hierarchy is presented. Confidence intervals for $\Delta m^2_{32}$, $\sin^2 \theta_{23}$, $\sin^2 \theta_{13}$ and…
Inverse problems and, in particular, inferring unknown or latent parameters from data are ubiquitous in engineering simulations. A predominant viewpoint in identifying unknown parameters is Bayesian inference where both prior information…
Accurate models are essential for design, performance prediction, control, and diagnostics in complex engineering systems. Physics-based models excel during the design phase but often become outdated during system deployment due to changing…
Inverse optimization (IO) is used to estimate unknown parameters of an optimization model from observed decisions. In the data-driven context, the estimated parameters are inherently uncertain, yet quantifying this uncertainty has received…
A promising approach to improve climate-model simulations is to replace traditional subgrid parameterizations based on simplified physical models by machine learning algorithms that are data-driven. However, neural networks (NNs) often lead…