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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2502.19713 |
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| _version_ | 1866929734016827392 |
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| author | Tung, Chi-Huan Yip, Sidney Huang, Guan-Rong Porcar, Lionel Shinohara, Yuya Sumpter, Bobby G. Ding, Lijie Do, Changwoo Chen, Wei-Ren |
| author_facet | Tung, Chi-Huan Yip, Sidney Huang, Guan-Rong Porcar, Lionel Shinohara, Yuya Sumpter, Bobby G. Ding, Lijie Do, Changwoo Chen, Wei-Ren |
| contents | Small-angle neutron scattering (SANS) is a powerful technique for probing the nanoscale structure of materials. However, the fundamental limitations of neutron flux pose significant challenges for rapid, high-fidelity data acquisition required in many experiments. To circumvent this difficulty, we introduce a Bayesian statistical framework based on Gaussian process regression (GPR) to infer high-quality SANS intensity profiles from measurements with suboptimal signal-to-noise ratios (SNR). Unlike machine learning approaches that depend on extensive training datasets, the proposed one-shot method leverages the intrinsic mathematical properties of the scattering function, smoothness and continuity, offering a generalizable solution beyond the constraints of data-intensive techniques. By examining existing SANS experimental data, we demonstrate that this approach can reduce measurement time by between one and two orders of magnitude while maintaining accuracy and adaptability across different SANS instruments. By improving both efficiency and reliability, this method extends the capabilities of SANS, enabling broader applications in time-sensitive and low-flux experimental conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_19713 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unlocking Hidden Information in Sparse Small-Angle Neutron Scattering Measurement Tung, Chi-Huan Yip, Sidney Huang, Guan-Rong Porcar, Lionel Shinohara, Yuya Sumpter, Bobby G. Ding, Lijie Do, Changwoo Chen, Wei-Ren Applied Physics Small-angle neutron scattering (SANS) is a powerful technique for probing the nanoscale structure of materials. However, the fundamental limitations of neutron flux pose significant challenges for rapid, high-fidelity data acquisition required in many experiments. To circumvent this difficulty, we introduce a Bayesian statistical framework based on Gaussian process regression (GPR) to infer high-quality SANS intensity profiles from measurements with suboptimal signal-to-noise ratios (SNR). Unlike machine learning approaches that depend on extensive training datasets, the proposed one-shot method leverages the intrinsic mathematical properties of the scattering function, smoothness and continuity, offering a generalizable solution beyond the constraints of data-intensive techniques. By examining existing SANS experimental data, we demonstrate that this approach can reduce measurement time by between one and two orders of magnitude while maintaining accuracy and adaptability across different SANS instruments. By improving both efficiency and reliability, this method extends the capabilities of SANS, enabling broader applications in time-sensitive and low-flux experimental conditions. |
| title | Unlocking Hidden Information in Sparse Small-Angle Neutron Scattering Measurement |
| topic | Applied Physics |
| url | https://arxiv.org/abs/2502.19713 |