Integrative Hidden Markov Mixture Modeling of Quantum Dot Intensity Fluctuations
A statistical model for jointly analyzing the fluctuating photon-emission intensity series of many semiconductor quantum dots. A hidden Markov backbone gives each latent state a zero-one-inflated Beta emission distribution shared across all dots, while state-transition dynamics are allowed to vary among a small number of clusters of dots, and the whole model is fit by an EM algorithm. Applied to 128 quantum dots it recovers a few shared intensity states and several distinct transition regimes, extending the classical single-dot blinking/flickering picture to a population-level, cluster-aware description.
Integrative Learning of Quantum Dot Intensity Fluctuations under Excitation via Tailored Dynamic
Develops a statistical method for analyzing the fluctuating photon-emission intensity of many semiconductor quantum dots at once. Because processed intensity series are non-Gaussian, truncated and no…