Roles of two successive phase transitions in new spin-Peierls system TiOBr

In this sturdy, we determine the roles of two successive phase transitions in the new spin-Peierls system TiOBr by electron and synchrotron X-ray diffraction analyses. Results show an incommensurate superstructure along the h- and k-directions between Tc1=27K and Tc2=47K, and a twofold superstructure which is related to a spin-Peierls lattice distortion below Tc1. The diffuse scattering observed above Tc2 indicates that a structural correlation develops at a high temperature. We conclude that Tc2 is a second-order lock-in temperature, which is related to the spin-Peierls lattice distortion with the incommensurate structure, and that Tc1 is from incommensurate to commensurate phase transition temperature accompanying the first-order spin-Peierls lattice distortion.Comment: 4 pages, 5 figure

Detection of Non-uniformity in Parameters for Magnetic Domain Pattern Generation by Machine Learning

We estimate the spatial distribution of heterogeneous physical parameters involved in the formation of magnetic domain patterns of polycrystalline thin films by using convolutional neural networks. We propose a method to obtain a spatial map of physical parameters by estimating the parameters from patterns within a small subregion window of the full magnetic domain and subsequently shifting this window. To enhance the accuracy of parameter estimation in such subregions, we employ large-scale models utilized for natural image classification and exploit the benefits of pretraining. Using a model with high estimation accuracy on these subregions, we conduct inference on simulation data featuring spatially varying parameters and demonstrate the capability to detect such parameter variations.Comment: 32 pages, 14 figure

Bayesian Inference of Absorption Spectra Based on Binomial Distribution

In this paper, we propose a Bayesian spectral deconvolution method for absorption spectra. In conventional analysis, the noise mechanism of absorption spectral data is never considered appropriately. In that analysis, the least-squares method, which assumes Gaussian noise from the perspective of Bayesian statistics, is frequently used. Since Bayesian inference is possible by introducing an appropriate noise model for the data, we consider the absorption process of a single photon to be a Bernoulli trial and develop a Bayesian spectral deconvolution method based on binomial distribution. We have evaluated our method on artificial data under several conditions by numerical experiments. The results show that our method not only allows us to estimate parameters with high accuracy from absorption spectral data, but also to infer them even from absorption spectral data with large absorption rates where the spectral structure is flattened, which was previously impossible to analyze