CMA-unfold: A covariance matrix adaptation unfolding algorithm for stacked calorimeter detectors

Stacking calorimeters, also referred to as bremsstrahlung cannons, widely used in inertial confinement fusion and ultra-intense laser plasma experiments, have become essential diagnostics for characterizing short bursts of high-energy photons and charged particles. Extracting the underlying energy spectrum from these detectors requires solving an ill-posed inverse problem, often complicated by noise, secondary particle contamination, and uncertainties in the detector response. In this work, we introduce an open-source unfolding framework (ggfauvel/CMA-unfold) [ggfauvel/CMA-unfold: Initial Public Release (2025)] based on the covariance matrix adaptation evolution strategy, designed to reconstruct photon spectra directly from depth-dose profiles without imposing restrictive parametric assumptions. The algorithm demonstrates high robustness, accurately recovering complex spectral shapes while tolerating percent-level deviations in individual detector layers. This approach provides a flexible and noise-resilient tool for the analysis of stacking calorimeter data, with particular relevance for bremsstrahlung diagnostics in high-intensity laser and inertial confinement fusion applications.