Scattering-based structural reconstruction by dimensional elevation

This study outlines a conceptually new approach for reconstructing the neutron scattering length density profile, Δρ(r), directly from small-angle neutron scattering (SANS) intensity profiles, I(Q). The method is built upon a universal operator A, fundamental to scattering processes, which relates I(Q) to Δρ(r) through the covariance matrix X ≡ Δρ(r)Δρ(r)†. In contrast to conventional SANS data analysis techniques, this approach eliminates the need to predefine a model of Δρ(r) in the regression process. This capability inherently addresses challenges often encountered in existing spectral inversion analysis, such as convergence to local minima due to incomplete analytical models, insufficient orthogonal basis vectors, or non-orthogonality among basis functions in model-free approaches. By extending spectral regression analysis from the vector space of I(Q) to the higher-dimensional space of AXA†, the PhaseLift framework imposes convexity on the regression process. This ensures the stable and computationally efficient reconstruction of the universal minimum Δρ(r) from I(Q). Numerical benchmarks and experimental validations confirm the reliability of this approach in tackling neutron scattering inverse problems. The method establishes a robust and flexible framework for advancing neutron scattering data analysis, with the potential to significantly enhance both the precision and efficiency of experiments across various scientific domains. It provides a solid foundation for further research into the interpretation and application of scattering data.

Authors

Institutions

Publication Details

Journal
The Journal of Chemical Physics
Published
2025-05-01
DOI
https://doi.org/10.1063/5.0257008
Primary Topic
Nuclear Physics and Applications
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Scattering-based structural reconstruction by dimensional elevation

Yuya Shinohara, Chi-Huan Tung, Guan-Rong Huang, Lionel Porcar et al.
The Journal of Chemical Physics
Nuclear Physics and Applications
article

Scattering-based structural reconstruction by dimensional elevation

Yuya Shinohara, Chi-Huan Tung, Guan-Rong Huang, Lionel Porcar, Changwoo Do, Pengwen Chen, Wei‐Ren Chen
article en

Abstract

This study outlines a conceptually new approach for reconstructing the neutron scattering length density profile, Δρ(r), directly from small-angle neutron scattering (SANS) intensity profiles, I(Q). The method is built upon a universal operator A, fundamental to scattering processes, which relates I(Q) to Δρ(r) through the covariance matrix X ≡ Δρ(r)Δρ(r)†. In contrast to conventional SANS data analysis techniques, this approach eliminates the need to predefine a model of Δρ(r) in the regression process. This capability inherently addresses challenges often encountered in existing spectral inversion analysis, such as convergence to local minima due to incomplete analytical models, insufficient orthogonal basis vectors, or non-orthogonality among basis functions in model-free approaches. By extending spectral regression analysis from the vector space of I(Q) to the higher-dimensional space of AXA†, the PhaseLift framework imposes convexity on the regression process. This ensures the stable and computationally efficient reconstruction of the universal minimum Δρ(r) from I(Q). Numerical benchmarks and experimental validations confirm the reliability of this approach in tackling neutron scattering inverse problems. The method establishes a robust and flexible framework for advancing neutron scattering data analysis, with the potential to significantly enhance both the precision and efficiency of experiments across various scientific domains. It provides a solid foundation for further research into the interpretation and application of scattering data.

The Journal of Chemical PhysicsVol. 162(17)
Oak Ridge National Laboratory (US), National Tsing Hua University (TW), Institut Laue-Langevin (FR), National Center for Theoretical Sciences (TW), National Center for Theoretical Sciences, Physics Division
Office of Science
Sustainable cities and communities
Openalex Percentile: Top 89%
Nuclear Physics and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.