Empirical-Bayes Unfolding of $\gamma$-ray Spectra

Abstract

Unfolding observed $\gamma$-ray spectra is an ill-conditioned Poisson inverse problem. Detector response effects and finite energy resolution make distinct non-negative emitted $\gamma$-ray spectra nearly indistinguishable after forward mapping, so direct inversion can strongly amplify statistical fluctuations. Here, we present an empirical-Bayes hierarchical unfolding method that preserves the Poisson counting structure, enforces non-negativity, and incorporates background through a joint ON/OFF likelihood. The prior on the emitted spectrum is centered on an automatically selected Richardson-Lucy reference spectrum, with an adaptive width that remains broad in weakly constrained regions. Posterior inference is performed with the No-U-Turn Sampler, and simultaneous uncertainty bands are reported for the resolution-limited unfolded spectrum. Our Bayesian method provides a robust and extensible framework for uncertainty quantification in unfolding, and a direct comparison with a recent frequentist regularized maximum-likelihood method gives highly consistent unfolded spectra in representative high- and low-statistics cases.

33 pages, 19 figures, submitted to Phys. Rev. C