Bayesian estimation of switching rates for blinking quantum emitters

Quantum emitters such as quantum dots or colour centres in diamond have a range of interesting applications ranging from quantum sensing to biomedical imaging to being th eactive material in the newest generation of screen technologies. However, the particular environment a quantum emitter is exposed to can lead to intermittency of its fluorescence known as blinking. Understanding blinking dynamics and its causes is essential for optimizing quantum emitters for technological applications. A first step in this process is the correct analysis and inference of underlying blinking rates which characterize the internal switching process of the quantum emitter from a dark to a bright state and vice versa. This thesis develops a methodology for inferring these rates from data using Bayesian analysis. It treats the underlying blinking process as a hidden Markov chain. Both discrete and continuous-time Markov models are developed and applied in order to infer switching rates from fluorescence time-series having a physically realistic range from very slow to very fast blinking. The hidden-chain Markov model developed here could find applications in other areas such as finance and computational biology.