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Desmearing Bonse-Hart USANS data using Bayesian Gaussian process regression
Chi-Huan Tung1, Guan-Rong Huang2, Yingrui Shang1
1Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA.
The Journal of Chemical Physics
|May 22, 2026
Summary
We developed a Bayesian method for ultra-small-angle neutron scattering (USANS) data analysis. This approach accurately recovers structural information from complex scattering data while quantifying uncertainties.
Area of Science:
- Materials Science
- Neutron Scattering Physics
- Data Analysis
Background:
- Ultra-small-angle neutron scattering (USANS) probes micrometer-scale structures.
- Slit-geometry optics in USANS cause significant anisotropic resolution smearing.
- Existing desmearing methods struggle with accuracy and uncertainty quantification.
Purpose of the Study:
- To develop a robust Bayesian framework for desmearing slit-geometry USANS data.
- To enable accurate recovery of intrinsic scattering intensity.
- To provide rigorous uncertainty quantification for USANS measurements.
Main Methods:
- Gaussian process regression models scattering intensity as a smooth random function.
- Instrumental point spread function is explicitly incorporated as a forward operator.
- A closed-form maximum a posteriori solution is derived.
Main Results:
- The Bayesian framework provides stable desmearing of slit-geometry USANS data.
- The method effectively suppresses experimental noise.
- Physically meaningful structural features are preserved under realistic conditions.
Conclusions:
- The proposed Bayesian approach offers a significant advancement for USANS data analysis.
- It overcomes limitations of traditional iterative desmearing methods.
- This framework enhances the reliability and interpretability of USANS and SANS studies.
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