Computational generation of tailored radionuclide libraries for alpha-particle and gamma-ray spectrometry

TL;DR

The proposed approach introduces a framework for computerized radionuclide library generation, thereby trivializing library-driven radionuclide identification and facilitating the spectral recognition of unregistered radionuclides in radiation spectrometry.

Abstract

Radionuclide identification is a radioanalytical method employed in various scientific disciplines that utilize alpha-particle or gamma-ray spectrometric assays, ranging from astrophysics to nuclear medicine. Radionuclide libraries in conventional radionuclide identification systems are crafted in a manual fashion, accompanying labor-intensive and error-prone user tasks and hindering library customization. This research presents a computational algorithm and the architecture of its dedicated software that can automatically generate tailored radionuclide libraries. Progenitor-progeny recurrence relations were modeled to enable recursive computation of radionuclide subsets. This theoretical concept was incorporated into open-source software called RecurLib and validated against four actinide decay series and twelve radioactive substances, including a uranium-glazed legacy Fiestaware, natural uranium and thorium sources, a $^{226}$Ra sample, and the medical radionuclides $^{225}$Ac, $^{177}$Lu, and $^{99\text{m}}$Tc. The developed algorithm yielded radionuclide libraries for all the tested specimens within minutes, demonstrating its efficiency and applicability across diverse scenarios. The proposed approach introduces a framework for computerized radionuclide library generation, thereby trivializing library-driven radionuclide identification and facilitating the spectral recognition of unregistered radionuclides in radiation spectrometry.

Figures (11)

• Figure 1: Conceptual illustrations of computational radionuclide library generation. a Logical relations between member radionuclides ($d_{0,0}$, $d_{0,1}$, $\cdots$, $d_{0,7}$ and $d_{1,0}$, $d_{1,1}$, $\cdots$, $d_{1,7}$), decay chains ($D_{0}$, $D_{1}$, and $D_{i \neq 0,1}$), radionuclide subsets ($X_{0}$ and $X_{h \neq 0}$), and a universal set ($U$). b Multiple radiation types can be involved in a single decay chain. c Recursive and static decay chains are schematically compared. d Most manual tasks in the conventional library construction approach are computerized by the developed algorithm.
• Figure 2: Third-party dependencies of the developed software RecurLib. Nuclear datasets are retrieved and coupled to RecurLib-constructed radionuclide subsets using the Evaluated Nuclear Structure Data File (ENSDF) of the National Nuclear Data Center (NNDC) via the Live Chart of Nuclides of the International Atomic Energy Agency (IAEA).
• Figure 3: Flowcharts for a radionuclide subsetting and b lineage construction. c A computationally generated 237Np lineage.
• Figure 4: Graphical representations of energy level feasibility validation. Schema plots of arbitrary parent-daughter pairs of (a) $d_{i,j}$/$d_{i,j+1}$ and (b) 99Mo/99Tc. The asterisk flag, $J^{\pi}$, and IT stand for an excited state, angular momentum-parity pair, and isomeric transition, respectively.
• Figure 5: Architectural features of RecurLib. a Python integration and cross-platform data exchange with arbitrary external software (S1--5). b Library trimming and plot customization using a marker registry and windowing.