Work

Desmearing Bonse–Hart USANS data using Bayesian Gaussian process regression

Year: 2026

Type: article

Source: The Journal of Chemical Physics

DOI: 10.1063/5.0320684

Authors: Chi-Huan Tung, Guan-Rong Huang, Yingrui Shang, Gernot Rother, Changwoo Do, Yangyang Wang, Jan-Michael Carrillo, Shabnam J. Semnani, Tao Zhang, Yuya Shinohara, Wei-Ren Chen

Institutions: Oak Ridge National Laboratory, National Tsing Hua University, Center for Nanophase Materials Sciences, University of California San Diego, The University of Texas at Arlington

Language: English

Topic: Nuclear Physics and Applications

Subfield: Radiation

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: closed

Funders: National Science and Technology Council, Office of Science

Awards: G121915436, Assistance Project for Processing Big Observational Data by Data Science — Collaborating with Artificial Intelligence for Image Reconstruction, Biomolecular Modeling, and Image Processing/Analysis on the Big Data Generated by Synchrotron Radiation( II ), G1647481203, G2049548230, Investigate Microstructural and Mechanical Responses of Complex Soft Matter to External Stimuli and Thermodynamic Variations Using Scattering, Simulation, and Machine Learning( I ), G6089394659, Assistance Project for Processing Big Observational Data by Data Science — Collaborating with Artificial Intelligence for Image Reconstruction, Biomolecular Modeling, and Image Processing/Analysis on the Big Data Generated by Synchrotron Radiation( I ), G6471876203, G7571479720

OpenAlex ID: W7162087408

FWCI: 8.105

Cites: 17

Cited by: 2

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