A Detailed Comparison of Multi-Dimensional Boltzmann Neutrino Transport Methods in Core-Collapse Supernovae

The mechanism driving core-collapse supernovae is sensitive to the interplay between matter and neutrino radiation. However, neutrino radiation transport is very difficult to simulate, and several radiation transport methods of varying levels of approximation are available. We carefully compare for the first time in multiple spatial dimensions the discrete ordinates (DO) code of Nagakura, Yamada, and Sumiyoshi and the Monte Carlo (MC) code Sedonu, under the assumptions of a static fluid background, flat spacetime, elastic scattering, and full special relativity. We find remarkably good agreement in all spectral, angular, and fluid interaction quantities, lending confidence to both methods. The DO method excels in determining the heating and cooling rates in the optically thick region. The MC method predicts sharper angular features due to the effectively infinite angular resolution, but struggles to drive down noise in quantities where subtractive cancellation is prevalent, such as the net gain in the protoneutron star and off-diagonal components of the Eddington tensor. We also find that errors in the angular moments of the distribution functions induced by neglecting velocity dependence are sub-dominant to those from limited momentum-space resolution. We briefly compare directly computed second angular moments to those predicted by popular algebraic two-moment closures, and find that the errors from the approximate closures are comparable to the difference between the DO and MC methods. Included in this work is an improved Sedonu code, which now implements a fully special relativistic, time-independent version of the grid-agnostic Monte Carlo random walk approximation.Comment: Accepted to ApJS. 24 pages, 19 figures. Key simulation results and codes are available at https://stellarcollapse.org/MCvsD

Cascade and Damping of Alfv\'{e}n-Cyclotron Fluctuations: Application to Solar Wind Turbulence Spectrum

With the diffusion approximation, we study the cascade and damping of Alfv\'{e}n-cyclotron fluctuations in solar plasmas numerically. Motivated by wave-wave couplings and nonlinear effects, we test several forms of the diffusion tensor. For a general locally anisotropic and inhomogeneous diffusion tensor in the wave vector space, the turbulence spectrum in the inertial range can be fitted with power-laws with the power-law index varying with the wave propagation direction. For several locally isotropic but inhomogeneous diffusion coefficients, the steady-state turbulence spectra are nearly isotropic in the absence of damping and can be fitted by a single power-law function. However, the energy flux is strongly polarized due to the inhomogeneity that leads to an anisotropic cascade. Including the anisotropic thermal damping, the turbulence spectrum cuts off at the wave numbers, where the damping rates become comparable to the cascade rates. The combined anisotropic effects of cascade and damping make this cutoff wave number dependent on the wave propagation direction, and the propagation direction integrated turbulence spectrum resembles a broken power-law, which cuts off at the maximum of the cutoff wave numbers or the $^4$He cyclotron frequency. Taking into account the Doppler effects, the model can naturally reproduce the broken power-law wave spectra observed in the solar wind and predicts that a higher break frequency is aways accompanied with a greater spectral index change that may be caused by the increase of the Alfv\'{e}n Mach number, the reciprocal of the plasma beta, and/or the angle between the solar wind velocity and the mean magnetic field. These predictions can be tested by future observations

Scaling in Plasticity-Induced Cell-Boundary Microstructure: Fragmentation and Rotational Diffusion

We develop a simple computational model for cell boundary evolution in plastic deformation. We study the cell boundary size distribution and cell boundary misorientation distribution that experimentally have been found to have scaling forms that are largely material independent. The cell division acts as a source term in the misorientation distribution which significantly alters the scaling form, giving it a linear slope at small misorientation angles as observed in the experiments. We compare the results of our simulation to two closely related exactly solvable models which exhibit scaling behavior at late times: (i) fragmentation theory and (ii) a random walk in rotation space with a source term. We find that the scaling exponents in our simulation agree with those of the theories, and that the scaling collapses obey the same equations, but that the shape of the scaling functions depend upon the methods used to measure sizes and to weight averages and histograms

Semiparametric Bayesian models for human brain mapping

Functional magnetic resonance imaging (fMRI) has led to enormous progress in human brain mapping. Adequate analysis of the massive spatiotemporal data sets generated by this imaging technique, combining parametric and non-parametric components, imposes challenging problems in statistical modelling. Complex hierarchical Bayesian models in combination with computer-intensive Markov chain Monte Carlo inference are promising tools.The purpose of this paper is twofold. First, it provides a review of general semiparametric Bayesian models for the analysis of fMRI data. Most approaches focus on important but separate temporal or spatial aspects of the overall problem, or they proceed by stepwise procedures. Therefore, as a second aim, we suggest a complete spatiotemporal model for analysing fMRI data within a unified semiparametric Bayesian framework. An application to data from a visual stimulation experiment illustrates our approach and demonstrates its computational feasibility

Double Diffusion Encoding Prevents Degeneracy in Parameter Estimation of Biophysical Models in Diffusion MRI

Purpose: Biophysical tissue models are increasingly used in the interpretation of diffusion MRI (dMRI) data, with the potential to provide specific biomarkers of brain microstructural changes. However, the general Standard Model has recently shown that model parameter estimation from dMRI data is ill-posed unless very strong magnetic gradients are used. We analyse this issue for the Neurite Orientation Dispersion and Density Imaging with Diffusivity Assessment (NODDIDA) model and demonstrate that its extension from Single Diffusion Encoding (SDE) to Double Diffusion Encoding (DDE) solves the ill-posedness and increases the accuracy of the parameter estimation. Methods: We analyse theoretically the cumulant expansion up to fourth order in b of SDE and DDE signals. Additionally, we perform in silico experiments to compare SDE and DDE capabilities under similar noise conditions. Results: We prove analytically that DDE provides invariant information non-accessible from SDE, which makes the NODDIDA parameter estimation injective. The in silico experiments show that DDE reduces the bias and mean square error of the estimation along the whole feasible region of 5D model parameter space. Conclusions: DDE adds additional information for estimating the model parameters, unexplored by SDE, which is enough to solve the degeneracy in the NODDIDA model parameter estimation.Comment: 22 pages, 7 figure

Bayesian Framework for Simultaneous Registration and Estimation of Noisy, Sparse and Fragmented Functional Data

Mathematical and Physical Sciences: 3rd Place (The Ohio State University Edward F. Hayes Graduate Research Forum)In many applications, smooth processes generate data that is recorded under a variety of observation regimes, such as dense sampling and sparse or fragmented observations that are often contaminated with error. The statistical goal of registering and estimating the individual underlying functions from discrete observations has thus far been mainly approached sequentially without formal uncertainty propagation, or in an application-specific manner by pooling information across subjects. We propose a unified Bayesian framework for simultaneous registration and estimation, which is flexible enough to accommodate inference on individual functions under general observation regimes. Our ability to do this relies on the specification of strongly informative prior models over the amplitude component of function variability. We provide two strategies for this critical choice: a data-driven approach that defines an empirical basis for the amplitude subspace based on available training data, and a shape-restricted approach when the relative location and number of local extrema is well-understood. The proposed methods build on the elastic functional data analysis framework to separately model amplitude and phase variability inherent in functional data. We emphasize the importance of uncertainty quantification and visualization of these two components as they provide complementary information about the estimated functions. We validate the proposed framework using simulation studies, and real applications to estimation of fractional anisotropy profiles based on diffusion tensor imaging measurements, growth velocity functions and bone mineral density curves.No embarg