MCViNE is an open source, object-oriented Monte Carlo neutron ray-tracing simulation software package. Its design allows for flexible, hierarchical representations of sophisticated instrument components such as detector systems, and samples with a variety of shapes and scattering kernels. Recently this flexible design has enabled several applications of MCViNE simulations at the Spallation Neutron Source (SNS) at Oak Ridge National Lab, including assisting design of neutron instruments at the second target station and design of novel sample environments, as well as studying effects of instrument resolution and multiple scattering. Here we provide an overview of the recent developments and new features of MCViNE since its initial introduction (Jiao et al 2016 Nucl. Instrum. Methods Phys. Res., Sect. A 810, 86-99), and some example applications.
This contribution describes the computational methodology behind an optimization procedure for a scattered beam collimator. The workflow includes producing a file that can be manufactured via additive methods. A conical collimator, optimized for neutron diffraction experiments in a high pressure clamp cell, is presented as an example. In such a case the scattering from the sample is much smaller than that of the pressure cell. Monte Carlo Ray tracing in MCViNE was used to model scattering from a Si powder sample and the cell. A collimator was inserted into the simulation and the number and size of channels were optimized to maximize the rejection of the parasitic signal coming from the complex sample environment. Constraints, provided by the additive manufacturing process as well as a specific neutron diffractometer, were also included in the optimization. The source code and the tutorials are available in c3dp (Islam (2019)).
Independently of the image modality (x-rays, neutrons, etc), image data analysis requires normalization, a preprocessing step. While the normalization can sometimes easily be generalized, the analysis is, in most cases, specific to an experiment and a sample. Although many tools (MATLAB, ImageJ, VG StudioK) offer a large collection of pre-programmed image analysis tools, they usually require a learning step that can be lengthy depending on the skills of the end user. We have implemented Jupyter Python notebooks to allow easy and straightforward data analysis, along with live interaction with the data. Jupyter notebooks require little programming knowledge and the steep learning curve is bypassed. Most importantly, each notebook can be tailored to a specific experiment and sample with minimized effort. Here, we present the pros and cons of the main methods to analyse data and show the reason why we have found that Jupyter Python notebooks are well suited for imaging data processing, visualization and analysis.
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