Calorimeter-based trigger system developments supported by exponential connections in non-parametric information geometry

Abstract

Trigger Systems (TrigSys) are an indispensable component of High-Energy Physics (HEP) experiments, serving as the initial decision layer to filter scientifically relevant events from the massive data rates produced by particle collisions. The robustness of these systems is paramount, as their performance critically dictates the efficiency and purity of the final physics dataset. However, TrigSys classifiers, especially those involving complex tasks like electron identification based uniquely on calorimetry, are highly susceptible to performance degradation caused by subtle shifts in the underlying probability density functions (PDFs) of detector response models. To address this vulnerability, a nonparametric information geometry framework is introduced for diagnosing and quantifying classifier instability under model transitions. In this work, we propose a validation model for calorimeter-based triggers, specifically focusing on electron signatures. Two trigger classifiers were constructed to simulate a transition from an initial physics model (v0) to an updated model (v1), both explicitly calibrated to preserve identical signal efficiencies. Using the exponential connection derived from the reference model (v0), the minimal geodesic distortions between the PDFs representing electron shower shapes are rigorously quantified. Even under equivalent signal efficiency calibration, the model transition is shown to induce fine-grained structural shifts in the underlying distributions. The main contribution of this work is the provision of a precise, non-parametric diagnostic tool capable of measuring these fundamental PDF distortions using geometric principles. By quantifying the stability of the trigger's underlying data representation, this novel methodology offers a significant advance over traditional efficiency metrics, enabling physicists to assess the true operational discrepancy and ensure the long-term integrity of HEP data acquisition.