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mcstas_gisans : combining ray tracing with the distorted-wave Born approximation using McStas and BornAgain for virtual GISANS experiments

Klausz, Milán ; Glavic, Artur ; Köhler, Sebastian LU ; Arnold, Thomas ; Paracini, Nicolò ; Gutfreund, Philipp ; Cárdenas, Marité LU ; Wolff, Max and Nylander, Tommy LU (2026) In Journal of Applied Crystallography 59. p.827-836
Abstract

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... (More)

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.

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author
; ; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
BornAgain, GISANS, grazing-incidence small-angle neutron scattering, instrumentation, McStas, mcstas_gisans, scientific computing
in
Journal of Applied Crystallography
volume
59
pages
10 pages
publisher
International Union of Crystallography
external identifiers
  • pmid:42232132
  • scopus:105042258343
ISSN
0021-8898
DOI
10.1107/S160057672600213X
language
English
LU publication?
yes
id
91bf03d7-38d4-477b-b132-38cb965e29d9
date added to LUP
2026-07-03 14:17:11
date last changed
2026-07-04 03:00:03
@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}},
}