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The vertical-cavity surface-emitting laser (VCSEL) is a novel type of semiconductor laser that is having a dramatic influence on many optical applications, including computing, communication, and sensing. Unfortunately, VCSELs suffer from a randomly fluctuating polarization whose dynamics are not fully understood. The model of stochastic polarization dynamics developed by San Miguel is rather complicated, and comparisons between experiment and simulations are quite difficult. One of the approaches to solve this problem was suggested in, where a simplified spin-eliminated linearized model (i.e., with dynamics reduced to that of a class-A laser) is used to analyze experimentally measured fluctuations and fluctuational switchings. In this talk, we present a recently developed technique that estimates the parameters of a nonlinear stochastic dynamical model by Bayesian inference, and demonstrate its application to the characterization of VCSELs. We start by considering the problem of diffusion of polarization in a potential well, onto which the dynamics of a class-A laser are usually mapped. We demonstrate the ability to infer laser parameters in numerical and analogue simulations, with the emphasis being placed on the role of large deviations. We specifically show that, contrary to one's intuition, the quality of inference can be improved by neglecting those data points in experimental time series that correspond to the rising part of large deviations. We then extend our technique to the full set of equations describing the polarization dynamics of a VCSEL in terms of
the motion of its Stokes vector on the Poincare sphere. Application of this technique to other standard problems encountered in characterizing semiconductor lasers, such as the identification of laser parameters from measurements of relaxational oscillations, is also discussed.
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