Search

Here, we discuss the comparability of data acquisitioned with the Modulation of IntEnsity with Zero Effort data, a neutron spin-echo (NSE) technique to neutron Time-of-Flight (ToF) spectroscopy data. As a NSE technique MIEZE records the intermediate scattering function $\mathcal{I}(Q, \tau)$ making it necessary to perform a Fourier transform to directly compare it to $S(Q, E)$, measured by ToF spectroscopy. Transforming either data set into the complementary parameter space requires detailed knowledge of detector efficiency, instrumental resolution, and background. We discuss these aspects by comparing measurements on pure water performed on the spectrometers RESEDA and TOFTOF under the same experimental conditions. Additionally, we discuss the data evaluation of spin-echo data beyond the SE approximation, which limits these techniques to small energy transfers.

Fitting a data set with a parametrized model can be seen geometrically as finding the global minimum of the chi^2 hypersurface, depending on a set of parameters {P_i}. This is usually done using the Levenberg-Marquardt algorithm. The main drawback of this algorithm is that despite of its fast convergence, it can get stuck if the parameters are not initialized close to the final solution. We propose a modification of the Metropolis algorithm introducing a parameter step tuning that optimizes the sampling of parameter space. The ability of the parameter tuning algorithm together with simulated annealing to find the global chi^2 hypersurface minimum, jumping across chi^2{P_i} barriers when necessary, is demonstrated with synthetic functions and with real data.

We present a study of the dynamics and structural changes for trans-1,2-dichloroethylene between high and low density liquids using neutron scattering techniques (diffraction, small angle neutron scattering and time of flight spectroscopy) and molecular dynamics simulations. We show that changes in the short range ordering of molecules goes along with a change of the molecular dynamics: both structure and dynamics of the high density liquid are more cooperative than those of the low density liquid. The microscopic mechanism underlying the cooperative motions in the high density liquid has been found to be related to the backscattering of molecules due to a strong correlation of molecular ordering., Comment: 5 pages and 3 figures. To be published in Physical Review B

Thin layers of water on biomolecular and other nanostructured surfaces can be supercooled to temperatures not accessible with bulk water. Chen et al. [PNAS 103, 9012 (2006)] suggested that anomalies near 220 K observed by quasi-elastic neutron scattering can be explained by a hidden critical point of bulk water. Based on more sensitive measurements of water on perdeuterated phycocyanin, using the new neutron backscattering spectrometer SPHERES, and an improved data analysis, we present results that show no sign of such a fragile-to-strong transition. The inflection of the elastic intensity at 220 K has a dynamic origin that is compatible with a calorimetric glass transition at 170 K. The temperature dependence of the relaxation times is highly sensitive to data evaluation; it can be brought into perfect agreement with the results of other techniques, without any anomaly., Comment: 4 pages, 3 figures. Phys. Rev. Lett. (in press)

The extraction of any physical information from quasielastic neutron scattering spectra is generally done by fitting a model to the data by means of chi-square minimization procedure. However, as pointed out by the pioneering work of D.S. Sivia et al., also another probabilistic approach based on Bayes theorem can be employed. In a nutshell, the main difference between the classical chi-square minimization and the Bayesian approach is the way of expressing the final results: In the first case, the result is a set of values of parameters with a symmetric error and a figure of merit such as chi-square, whereas in the second case the results are presented as probability distribution functions (PDF) of both, parameters and merit figure. In this contribution, we demonstrate how final PDFs are obtained by exploring all possible combinations of parameters that are compatible with the experimental error. Three advantages of this method will be emphasized: First, correlations between parameters are automatically taken into account, which implies, for example, that parameter errors are correctly calculated, correlations show up in a natural way and ill defined parameters are immediately recognized from their PDF. Second, it is possible to calculate the likelihood of a determined physical model, and therefore to select the one among many that fits the data best with a minimal number of parameters, in a correctly defined probabilistic way.