Bayesian inference of particle size distributions from dynamic light scattering

Autocorrelation functions from dynamic light scattering experiments have previously been analyzed either by performing least-squares fits to determine the mean and variance of the particle size distribution, or by using constrained regularization techniques to infer the size distribution. We present open-source tools for performing Bayesian inference of particle size distributions while rigorously incorporating smoothness and non-negativity constraints on the inferred distributions. We successfully apply these tools to simulated autocorrelation functions at multiple scattering angles. We intend to release these tools for use by the soft condensed matter and biophysics communities in the near future.