Characterisation of radioactive decay series by digital autoradiography, part 1: a theoretical approach using time and space coincidence (TSC) analysis

TL;DR

This paper presents a theoretical method for detecting and analyzing radioactive decay series using time and space coincidence analysis, improving efficiency over classical methods and distinguishing true from random coincidences.

Contribution

It introduces a novel, more efficient algorithm for detecting TSCs in autoradiography, enabling better characterization of decay chains and quantification of true versus random coincidences.

Findings

True TSCs vary linearly with activity

Random TSCs follow Poisson statistics and vary quadratically

True TSCs dominate at low uranium concentrations (<5000 ppm)

Abstract

The three natural decay chains have short-lived daughter elements, and the existence of these radioelements makes it possible for alpha and beta particle emissions to be generated at the same place and the same time. We show theoretically that such time and space coincidences (TSCs) can be detected efficiently by suitable autoradiographic systems using an algorithm that is six times more efficient than an approach based on the classical slicing of space-time. Two types of TSC coexist: true TSCs, resulting from the decay of short-lived daughter elements, and random TSCs. True TSCs are predictable and their numbers vary linearly with activity; the prediction of true alpha/alpha and alpha/alpha/alpha TSCs of the 235U chain is presented. Random coincidences are also predictable using Poisson's law. They vary quadratically as a function of activity. Examination of the case of an uranium ore at secular equilibrium shows that the observed alpha/alpha coincidences result from the sum of random and true TSCs. For high uranium contents, random coincidences predominate. For uranium at secular equilibrium, the theoretical calculation shows that true TSCs predominate for contents below ~5000 ppm.