25 citations · 35 across the 16 of their papers we have counts for
17 papers
Resolving the Effects of the Mixing Process on Hierarchical Structures in Polymer Nanocomposites by Bayesian Ultra-Small-Angle X-ray Scattering Analysis
Yui Hayashi, Kazuki Mita, Shigeo Kuwamoto +1
Bayesian inference was combined with Unified Fit to resolve the effects of the mixing history on polymer nanocomposites whose ultra-small-angle X-ray scattering profiles are nearly…
Computed Tomography Reconstruction Algorithm Using Markov Random Field Model
Taiga Shimomiya, Taichi Kusumi, Masayuki Uesugi +4
X-ray computed tomography (CT) reveals the materials' internal structures non-destructively from a tilt series of projected images. Filtered back projection (FBP) is a widely-adopt…
GPU-Accelerated Sequential Monte Carlo for Bayesian Spectral Analysis
Tomohiro Nabika, Yui Hayashi, Masato Okada
Bayesian spectral deconvolution provides a data-driven framework for mathematical model selection and parameter estimation from spectral data. Although highly versatile, it becomes…
Automatic Termination Strategy of Inelastic Neutron-scattering Measurement Using Bayesian Optimization for Bin-width Selection
Kensuke Muto, Hirotaka Sakamoto, Kenji Nagata +2
Currently, an excessive amount of event data is being obtained in four-dimensional inelastic neutron-scattering experiments. A method for automatic bin-width optimization of multid…
Mesoscopic Bayesian Inference by Solvable Models
Shun Katakami, Shuhei Kashiwamura, Kenji Nagata +2
The rapid advancement of data science and artificial intelligence has affected physics in numerous ways, including the application of Bayesian inference, setting the stage for a re…
Bayesian Inference for Small-Angle Scattering Data II: Core-Shell Samples
Keigo Oyama, Yui Hayashi, Shigeo Kuwamoto +4
Small-angle scattering (SAS) techniques, which utilize neutrons and X-rays, are employed in various scientific fields, including materials science, biochemistry, and polymer physic…
Basis Function Dependence of Estimation Precision for Synchrotron-Radiation-Based Mössbauer Spectroscopy
Binsheu Shieh, Ryo Masuda, Satoshi Tsutsui +4
Mössbauer spectroscopy is a technique employed to investigate the microscopic properties of materials using transitions between energy levels in the nuclei. Conventionally, in sync…
Quantitative Selection of Sample Structures in Small-Angle Scattering Using Bayesian Methods
Yui Hayashi, Shun Katakami, Shigeo Kuwamoto +3
Small-angle scattering (SAS) is a key experimental technique for analyzing nano-scale structures in various materials.In SAS data analysis, selecting an appropriate mathematical mo…
Quantifying physical insights cooperatively with exhaustive search for Bayesian spectroscopy of X-ray photoelectron spectra
Hiroyuki Kumazoe, Kazunori Iwamitsu, Masaki Imamura +4
We analyzed the X-ray photoemission spectra (XPS) of carbon 1s states in graphene and oxygen-intercalated graphene grown on SiC(0001) using Bayesian spectroscopy. To realize highly…
Sequential Experimental Design for Spectral Measurement: Active Learning Using a Parametric Model
Tomohiro Nabika, Kenji Nagata, Shun Katakami +2
In this study, we demonstrate a sequential experimental design for spectral measurements by active learning using parametric models as predictors. In spectral measurements, it is n…
Efficient NMR measurement and data analysis supported by the Bayesian inference : The case of the heavy fermion compound YbCo2Zn20
H. Ueda, S. Katakami, S. Yoshida +4
We propose a data-driven technique to infer microscopic physical quantities from nuclear magnetic resonance(NMR) spectra, in which the data size and quality required for the Bayesi…
Bayesian Inference for Small-Angle Scattering Data
Yui Hayashi, Shun Katakami, Shigeo Kuwamoto +3
In this paper, we propose a method for estimating model parameters using Small-Angle Scattering (SAS) data based on the Bayesian inference. Conventional SAS data analyses involve p…
Bayesian Inference of Absorption Spectra Based on Binomial Distribution
Tomohiro Nabika, Kenji Nagata, Shun Katakami +2
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 consid…
Bayesian Inference on Hamiltonian Selections for Mössbauer Spectroscopy
Ryota Moriguchi, Satoshi Tsutsui, Shun Katakami +3
Mössbauer spectroscopy, which provides knowledge related to electronic states in materials, has been applied to various fields such as condensed matter physics and material science…
Bayesian Spectral Deconvolution of X-Ray Absorption Near Edge Structure Discriminating High- and Low-Energy Domains
Shuhei Kashiwamura, Shun Katakami, Ryo Yamagami +6
In this paper, we propose a Bayesian spectral deconvolution considering the properties of peaks in different energy domains. Bayesian spectral deconvolution regresses spectral data…
Intrinsic regularization effect in Bayesian nonlinear regression scaled by observed data
Satoru Tokuda, Kenji Nagata, Masato Okada
Occam's razor is a guiding principle that models should be simple enough to describe observed data. While Bayesian model selection (BMS) embodies it by the intrinsic regularization…
Bayesian Hamiltonian Selection in X-ray Photoelectron Spectroscopy
Yoh-ichi Mototake, Masaichiro Mizumaki, Ichiro Akai +1
Core-level X-ray photoelectron spectroscopy (XPS) is a useful measurement technique for investigating the electronic states of a strongly correlated electron system. Usually, to ex…