Sparse regression algorithm for activity estimation in $\gamma $ spectrometry

TL;DR

This paper introduces a sparse regression approach using a modified LASSO method to accurately estimate radioactive source activity from spectrometry data, especially under pileup distortions.

Contribution

It proposes a novel sparse regression framework with theoretical bounds for activity estimation in gamma spectrometry, addressing pileup effects.

Findings

Method performs well on simulations

Method yields accurate activity estimates

Theoretical bounds support practical effectiveness

Abstract

We consider the counting rate estimation of an unknown radioactive source, which emits photons at times modeled by an homogeneous Poisson process. A spectrometer converts the energy of incoming photons into electrical pulses, whose number provides a rough estimate of the intensity of the Poisson process. When the activity of the source is high, a physical phenomenon known as pileup effect distorts direct measurements, resulting in a significant bias to the standard estimators of the source activities used so far in the field. We show in this paper that the problem of counting rate estimation can be interpreted as a sparse regression problem. We suggest a post-processed, non-negative, version of the Least Absolute Shrinkage and Selection Operator (LASSO) to estimate the photon arrival times. The main difficulty in this problem is that no theoretical conditions can guarantee consistency in sparsity of LASSO, because the dictionary is not ideal and the signal is sampled. We therefore derive theoretical conditions and bounds which illustrate that the proposed method can none the less provide a good, close to the best attainable, estimate of the counting rate activity. The good performances of the proposed approach are studied on simulations and real datasets.