Prior-Based Multi-Voltage Threshold Sampling as a Structured Inverse Problem

Abstract

Prior-based Multi-Voltage Threshold (MVT) sampling reconstructs pulse parameters from sparse threshold-crossing times rather than full waveforms, making parameter recovery inherently a model-dependent inverse problem. However, prior-based MVT has lacked a formal mathematical statement, leaving identifiability, stochastic error propagation, and threshold design without a unified theoretical foundation. We formalize prior-based MVT for strictly unimodal pulse families as a structured inverse problem. On that foundation, we develop the first unified theory of prior-based MVT, comprising deterministic identifiability conditions, a stochastic timing-error model with leading-order mismatch bias, and a nuisance-profiled threshold-design theory centered on an effective-information equation for robust single-event and partial-trigger multi-event operation. We instantiate the framework for the bi-exponential pulse model, derive executable design recipes, and validate the resulting predictions on a 10,000-pulse $^{22}$Na/LYSO/SiPM dataset. The experiments confirm that the framework yields useful threshold designs in the photopeak regime while also revealing the regime boundary at which partial triggering and model mismatch limit the predictive power of Fisher-guided optimization. These results provide the first unified mathematical foundation for prior-based MVT and recast it from an empirical threshold heuristic as a principled inferential framework.