Regularized Unfolding of gamma-ray Spectra for Nuclear Physics Applications

TL;DR

This paper presents a regularized maximum-likelihood unfolding method for gamma-ray spectra that improves reconstruction quality and uncertainty calibration over standard techniques, especially for low-complexity spectra.

Contribution

The paper introduces a novel RMLE-based unfolding framework that enforces physical constraints and models background, outperforming existing methods for simpler spectra.

Findings

RMLE produces smoother spectra with calibrated confidence intervals.

RMLE outperforms traditional methods in low-complexity scenarios.

Interval coverage remains correct for high-complexity data.

Abstract

Reconstructing gamma-ray spectra from detector measurements is an ill-posed inverse problem. Standard methods, such as Folding Iteration with Compton Subtraction (FICS), provide point estimates but lack calibrated uncertainties and may bias the spectrum. We introduce an unfolding framework based on regularized maximum-likelihood estimation (RMLE) that enforces non-negativity and detector-response constraints while explicitly modeling background and contaminant contributions. Simulations and analytical results show that RMLE yields smoother reconstructions with well-calibrated confidence intervals and outperforms existing techniques for low-complexity spectra. Although high-complexity data remain challenging, the intervals produced by RMLE maintain correct coverage.