Advanced EVS Pixel Modeling to Improve Dim RSO Detection up to 2.9× and Enable Accurate Photometric Reconstruction for SDA

gls{EVS} have been increasingly utilized for \gls{SDA} applications over the past several years. Newer models demonstrate improved performance compared to early generation systems; however, the absolute limits of their sensitivity with respect to fundamental physical parameters (i.e. on-chip photon flux) for detecting dim point source objects has not been fully quantified.Additionally, the complex exercise of optimizing tunable sensor biases for SDA has not been explored for the newest generation sensors. This work shows that with optimized biasing, scan rate, and a novel denoising strategy, the IMX636 sensor is capable of detecting point sources that produce on the order of several hundred photons per second on the focal plane. Our detailed characterization allows users of the technology to predict detection limits when paired with varied optical systems. The result is included in an open-source software tool. Additionally, bias optimization is shown to extend sensitivity by 1.05 visual magnitudes, m _v , and when coupled with the our denoising method, enables detection of 2.9× more star streaks compared with default biases. The detailed modeling of pixel level performance further enables a method for reconstructing object brightness from event camera output by calibrating the sensor's dark current and incorporating a model that accounts for the pixel's illumination dependent temporal response and logarithmic compression. Ultimately, the ability to accurately extract photometric information from an event camera can extend to other characterization methods including polarimetry and spectral analysis with proven EVS benefits of excellent temporal resolution, low power, sparse data output.