Small Angle Neutron Scattering in McStas: optimization for high throughput virtual experiments
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866929670174277632 |
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| author | Robledo, Jose Lieutenant, Klaus Willendrup, Peter |
| author_facet | Robledo, Jose Lieutenant, Klaus Willendrup, Peter |
| contents | In this work we present the development of small angle scattering components in McStas that describe the neutron interaction with 70 different form and structure factors. We describe the considerations taken into account for the generation of these components, such as the incorporation of polydispersity and orientational distribution effects in the Monte Carlo simulation. These models can be parallelized by means of multi-core simulations and graphical processing units (GPUs). The acceleration schemes for the aforementioned models are benchmarked, and the resulting performance is presented. This allows for the estimation of computation times in high-throughput virtual experiments. The presented work enables the generation of large datasets of virtual experiments that can be explored and used by machine learning algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_06054 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Small Angle Neutron Scattering in McStas: optimization for high throughput virtual experiments Robledo, Jose Lieutenant, Klaus Willendrup, Peter Instrumentation and Detectors In this work we present the development of small angle scattering components in McStas that describe the neutron interaction with 70 different form and structure factors. We describe the considerations taken into account for the generation of these components, such as the incorporation of polydispersity and orientational distribution effects in the Monte Carlo simulation. These models can be parallelized by means of multi-core simulations and graphical processing units (GPUs). The acceleration schemes for the aforementioned models are benchmarked, and the resulting performance is presented. This allows for the estimation of computation times in high-throughput virtual experiments. The presented work enables the generation of large datasets of virtual experiments that can be explored and used by machine learning algorithms. |
| title | Small Angle Neutron Scattering in McStas: optimization for high throughput virtual experiments |
| topic | Instrumentation and Detectors |
| url | https://arxiv.org/abs/2501.06054 |