Data Analysis and Processing Method for Two-dimensional Position Sensitive Detector of Neutron Texture Diffractometer at China Advanced Research Reactor

Abstract

The neutron texture diffractometer has unique advantage in the texture research for the key materials in the fields of aerospace, transportation, nuclear industry and so on. In order to improve the detection efficiency, the detection system of the neutron texture diffractometer at China Advanced Research Reactor (CARR) has been upgraded from a single 3He counter tube to a large-area two-dimensional position sensitive detector during 2020-2021. However, due to the difference of configuration and hardware design of the neutron texture diffractometer at the major neutron scattering laboratories in the world, the formats of obtained experimental data are also different, which results in the significant difference of the data analysis and processing. Therefore, the new method and software should be proposed to analyze the experimental data collected from two-dimensional position sensitive detector of neutron texture diffractometer at CARR.The results of texture measurement using neutron texture diffractometer generally were represented by pole figure. The pole figure window resolution of the texture diffractometer is an important factor affecting the results of texture measurement. Due to the diffraction signal of larger range angles along the longitudinal direction (α direction) can be detected by the two-dimensional position-sensitive detector, therefore, in order to meet the demand of texture measurement for resolution, the diffraction data should be firstly divided into several pieces in longitudinal direction to adjust the α direction resolution of the pole figure window. Then, during data preprocessing, it is key to accurately obtain the integral intensity of the diffraction peak for getting an accurate pole figure. However, the position of diffraction peaks may shift slightly due to the possible eccentricity during sample installation and uneven background of diffraction spectrum caused by noise, which result in the inaccurate fitting results. Moreover, generally the massive experimental data were collected by the two-dimensional sensitive detector during texture measurement. Thus, the parameters of background and peaks position should be strictly constricted for data corresponding to each-phi angle before fitting diffraction spectrum. The constrained parameters obtained through optimization of polynomial background function and peak shape function by fitting sum-phi data (the sum of intensity for all phi angle according to each chi angle) were used to fit each-phi data. Finally, the fitting integral intensity were converted and normalized for all each-phi data to get pole figure. Based on these design ideals, the data processing and analysis software was developed using Python language for neutron texture measurement with two-dimensional position sensitive detector. The experimental data of magnesium alloy was analyzed and processed with this software system, and the results verified the reliability of software.In a word, according to the experimental requirements of neutron texture diffractometer equipped with two-dimensional position sensitive detector at CARR, the experimental data analysis method has been established, and the data analysis and processing software has been developed. Especially, the resolution adjustment method of pole figure window along α direction has been achieved based on two-dimensional position sensitive detector. The batch processing of conversion from neutron counting to the integrated intensity of diffraction peaks is realized. In addition, the pole density normalization and the drawing of pole figures can be achieved smoothly. All of these meet the functional requirements of the upgraded neutron texture diffractometer and provide the guidance for the similar data analysis and processing.

Keywords

• neutron texture diffractometer
• texture data analysis
• two-dimensional position sensitive detector
• pole figure