Development of a photon-counting deadtime noise model that extends dynamic range and resolution in atmospheric lidar

This work derives and validates a noise model that encapsulates the deadtime of non-paralyzable detectors with random photon arrivals to enable advanced processing, such as maximum-likelihood estimation, of high-resolution atmospheric lidar profiles, while accounting for deadtime bias. This estimator was validated across a wide dynamic range at high resolution (4 mm in range and 17 ms in time). Experiments demonstrate that the noise model outperforms the current state-of-the-art for very short time-of-flight (2 ns) and extended targets (1 µs). The proposed noise model also produces accurate deadtime correction for very short integration times. This work sets the foundation for further study into accurate retrievals of high flux and dynamic atmospheric features, e.g., clouds and aerosol layers.