Decomposing CR-39 p-B Fusion Spectra with Hierarchical Bayesian Inference

CR-39 nuclear track detectors are widely used in fusion experiments for energetic particle detection. However, resolving individual species from multi-ion spectra remains a challenge when background protons and carbons dominate the signal. I present a hierarchical Bayesian inference approach using Markov Chain Monte Carlo sampling to probabilistically infer individual ion distributions, reducing user bias and improving uncertainty quantification for fusion signatures. This method enhances the accuracy and reliability of CR-39 diagnostics, enabling more precise fusion performance analysis in future IFE experiments across a spectrum of different fuels.