Optimal state detection without likelihood estimation

We show that adaptive Bayesian estimation in fluorescence state detection reduces to a comparison of elapsed time with two arithmetic time sequences fixed by the bright and dark count rates and confidence parameters ${\epsilon }_d$ and ${\epsilon }_b$. No probabilities are computed in real time and the resulting implementation, which we term sequential deadline detection, is provably optimal in mean detection time. The analysis permits closed-form expressions for detection-time distributions, mean detection times, error rates of both channels accounting for miscalibrated rates, decay between the states during detection, and afterpulsing in single photon counting modules. The predictions are validated by Monte Carlo simulation and demonstrated by fluorescence detection with Ba${}^{+}$