Monte Carlo Simulation of Optical Photon Detection Efficiency: A Geant4 Study with Surface Roughness, Incidence Angles, and Wavelength Dependence

Abstract This paper presents a comprehensive Monte Carlo simulation of optical photon detection using the Geant4 toolkit. Multiple scenarios were designed to analyze the influence of detector surface roughness, incidence angles, and photon wavelengths on detection efficiency. Simulated configurations included smooth, rough, and nanostructured surfaces, with incidence angles of 0°, 30°, and 60°, and wavelengths ranging from 450 nm to 630 nm. The outputs were processed using Python-based analysis pipelines to generate key performance indicators (KPIs) and statistical summaries. Results indicate that smooth and nanostructured surfaces significantly improve detection efficiency, while rough surfaces reduce photon transmission. Angle-dependent behavior shows strong degradation at oblique incidences, and wavelength-dependent performance aligns with published quantum efficiency curves of photodetectors. These findings validate the methodology and suggest potential applications in optical communication systems, medical imaging, and detector optimization. The paper concludes with recommendations for integrating machine learning approaches to further enhance predictive modeling and design automation.