New Application of Novel Volume Detectors Improves Data Quality in Neutron Powder Diffraction
In this work, we present a novel application of neutron volume detectors, as realized for the TOF neutron powder diffractometer POWTEX [1, 2]. This application leverages the detector's additional depth dimension to reconstruct neutron trajectories and enhance analysis capabilities. The key advantage lies in the ability to exploit this extra dimension to extract information about the origin of the scattering source within the experiment. While directly detecting the neutron's path within the detector is not achievable, the volume detector allows us to statistically correlate multiple neutron events within its depth to a single neutron trajectory. A neutron trajectory can be imagined as a collection of neutrons, all having undergone identical diffraction conditions and behaving identically within the experiment's resolution. The volume detector's ability to capture events along the depth direction allows us to correlate these events to the same neutron trajectory. This correlation empowers us to distinguish "good" neutrons that follow the expected diffraction pattern from "bad" outliers. By identifying and removing these outliers automatically, the volume detector significantly improves data quality. This translates to cleaner datasets and facilitates superior analysis, ultimately leading to deeper scientific insights in neutron powder diffraction experiments. Furthermore, it alleviates the burden on researchers by automating the removal of "bad" events, which were traditionally identified and removed through manual processes. This application will enhance the data treatment and refinement, both for conventional and multi-dimensional approaches [3]. This advancement holds immense potential for the neutron science community, offering researchers a powerful tool to unlock new avenues of analysis and discovery. [1] Houben A., Schweika W., Brückel Th. and Dronskowski R., Nucl. Instr. and Meth. A, 2012, 680, 124. [2] Modzel G., Henske M., Houben A., Klein M., Köhli M., Lennert P., Meven M., Schmidt C. J., Schmidt U. and Schweika W., Nucl. Instr. Meth. A, 2014, 743, 90. [3] Houben, A.; Jacobs, P.; Meinerzhagen, Y.; Nachtigall, N.; Dronskowski, R., J. Appl. Cryst. 2023, 56, 633–642.
Institutions
- RWTH Aachen University (DE)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-06-24
- DOI
- https://doi.org/10.5281/zenodo.15728095
- Primary Topic
- Artificial Intelligence Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00