## Simulation types: Small-angle scattering

While BornAgain is designed for GISAS experiments (using the Distorted Wave Born Approximation), it naturally also contains the regular (plane wave) Born Approximation. Accordingly, BornAgain can also simulate standard small-angle scattering (SAS).

However, there exist several other specialized SAS softwares. Therefore we do not advertise BornAgain for analysing SAS experiments, and in general we do not provide user support for this application domain. We rather recommend SASView, which is institutionally supported by the European Spallation Source, and was designated as standard SAS software in the European SINE2020 project.

Yet BornAgain can be an appropriate choice in cases where the sample structure or the experimental conditions are not covered by other software. For example, other softwares provide no, or limited, support for polarized SANS. Here, we show how such experiments can be simulated with BornAgain.

### Basic example

To simulate conventional small-angle scattering with BornAgain, we turn source and detector by 90 degrees with respect to grazing-incidence geometry.

The following basic example simulates scattering from a dilute random assembly of monodisperse, oriented dodecahedra.

Scattering intensity

auto/Examples/scatter2d/DodecahedraSAS.py

- Introduction
  - Functionality overview
  - Architectural overview

- Install or build
  - Install Python
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  - Simulation model
    - Simulation classes
      - Specular
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        - Examples
          - GISAS
          - without DWBA terms
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        - Off-specular
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      - Background
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  - Instrument model
    - Beam
      - Footprint
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      - TOF reflectometry
      - TOF resolution
      - Beam Angular Spread
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    - Detectors
      - SphericalDetector
      - 2d detector resolution
      - Offspec detector
    - Neutron polarization
      - Spin asymmetry example
    - Examples
      - 2D detector resolution
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        - Magnetic layer
        - Ditto, realistic instrument
        - Polarized scattering
  - Sample model
    - Materials
      - Magnetization
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      - Layer
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      - Interface model
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      - Examples
        - GISAS
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    - Particles
      - Form factor
        - Hard particles
          - Bipyramid4
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    - Particle assemblies
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      - 2D lattice
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        - Multi-layer lattice
        - Position variance
      - Radial paracrystal
      - 2D paracrystal
      - Others
        - Size-distribution model: decoupling approximation
        - Size-distribution model: local monodisperse approximation
        - Size-distribution model: size-spacing coupling approximation
  - Model modifiers
    - Rotation
    - Distributions
  - Data types
    - Class Datafield
      - Export
      - Find intensity peaks
    - SLD profile
      - SLD profile with rough interfaces
      - SLD profile with a sliced particle layer
  - Plotting
    - Functions
    - Configuration
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    - LaTeX markup
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    - Plot axes in different units
  - Fitting
    - Data import
      - TIFF
    - Minimizer settings
    - Multiple datasets
    - Examples
      - Fitting with masks
      - Custom objective function
      - Fitting along slices
      - GISAS 2D fits
      - Experiment at GALAXI
      - Find background
      - Minimizer settings
      - Bayesian Sampling
      - Consecutive fitting
      - Considering uncertainties
      - External minimizer
      - External minimizer: plotting
      - Simultaneous fit of two datasets
      - Spin asymmetry fit example
      - Reflectometry: Fit Pt layer
      - Reflectometry: Fit honeycomb

- Literature
  - Babonneau2010
  - Babonneau2013
  - Croset2021
  - Lazzari2002
  - Lazzari2006
  - Pospelov2020
  - Renaud2009
  - Sears1992
  - Wuttke2021

- How to
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    - Set Python environmental variables under Windows
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