mcstas_gisans : combining ray tracing with the distorted-wave Born approximation using McStas and BornAgain for virtual GISANS experiments
Abstract
The mcstas_gisans framework is a collection of Python scripts and modules to facilitate the simulation of grazing-incidence small-angle neutron scattering (GISANS) experiments. This approach combines McStas instrument simulation with BornAgain sample modeling capabilities. The Monte Carlo Particle Lists format for particle trajectory allows exchange between simulations that enables seamless transition from instrument modeling to sample scattering analysis. The Python-based processing utilities handle data transformation, scaling to virtual experiment times for absolute intensities, and visualization. The required software environment is managed through Conda, ensuring reproducible deployments across platforms. This integrated approach facilitates accurate simulation and analysis and enables the comparison of the GISANS capability of different neutron scattering instruments.
Fields of Study & Subjects
Nuclear Physics and Applications
Quantum
superfluid
helium dynamics
Spectroscopy and Quantum Chemical Studies
Publication details
- Journal / source
- Journal of Applied Crystallography
- Publisher
- International Union of Crystallography (IUCr)
- Volume
- 59
- Issue
- 3
- Pages
- 827–836
- Publication date
- 2 April 2026
- Language
- EN