@article{91bf03d7-38d4-477b-b132-38cb965e29d9,
  abstract     = {{<p>The mcstas_gisans framework is a collection of Python scripts and modules tofacilitate the simulation of grazing-incidence small-angle neutron scattering(GISANS) experiments. This approach combines McStas instrument simulationwith BornAgain sample modeling capabilities. The Monte Carlo Particle Listsformat for particle trajectory allows exchange between simulations that enablesseamless transition from instrument modeling to sample scattering analysis. ThePython-based processing utilities handle data transformation, scaling to virtualexperiment times for absolute intensities, and visualization. The required software environment is managed through Conda, ensuring reproducible deployments across platforms. This integrated approach facilitates accurate simulationand analysis and enables the comparison of the GISANS capability of differentneutron scattering instruments.</p>}},
  author       = {{Klausz, Milán and Glavic, Artur and Köhler, Sebastian and Arnold, Thomas and Paracini, Nicolò and Gutfreund, Philipp and Cárdenas, Marité and Wolff, Max and Nylander, Tommy}},
  issn         = {{0021-8898}},
  keywords     = {{BornAgain; GISANS; grazing-incidence small-angle neutron scattering; instrumentation; McStas; mcstas_gisans; scientific computing}},
  language     = {{eng}},
  pages        = {{827--836}},
  publisher    = {{International Union of Crystallography}},
  series       = {{Journal of Applied Crystallography}},
  title        = {{mcstas_gisans : combining ray tracing with the distorted-wave Born approximation using McStas and BornAgain for virtual GISANS experiments}},
  url          = {{http://dx.doi.org/10.1107/S160057672600213X}},
  doi          = {{10.1107/S160057672600213X}},
  volume       = {{59}},
  year         = {{2026}},
}