Optimal use of data for PTC characterization of photon counting capable sensors

Photon counting capable sensors tend to operate in a regime known as Deep Sub-Electron Read Noise or DSERN. In this regime, discrete nature of individual photons remains in the observed data, enabling quantification and new quantum experimental applications. A critical component to accurate photon counting is the knowledge (at the pixel level) of the conversion gain and read noise. This work compares the use of the Photon Counting Histogram Expectation Maximization (PCH-EM) algorithm to other DSERN characterization methods focusing on key performance parameters of conversion gain and read noise. A sensitivity analysis using synthetic data explores the dependence of the uncertainty in the conversion gain estimate on the magnitude of read noise and relative illumination levels. Additionally, the PCH-EM approach is validated using experimental data captured from a CMOS DSERN sensor. The results reveal the benefits of utilizing all available information in the raw sensor data and provide guidance on the optimal characterization method for different read noise regimes.