Improved mean squared displacement analysis for anomalous single-particle trajectories

The mean squared displacement (MSD) is a cornerstone in the analysis of diffusion processes in complex media. When the system is heterogeneous and, in particular, when single-particle trajectories are short, it is essential to extract maximal information from each measured trajectory. This is typically done by time-averaging squared increments and examining the scaling of the time-averaged MSD in log-log space. However, classical regression methods perform poorly in this setting because time-averaging introduces correlations aggravated by those inherent to anomalous diffusion. We tackle these limitations by applying a generalized least-squares framework, which substantially reduces variance and bias in diffusion parameter estimates, especially for short (≈100 points) and ultra-short (≈10 points) trajectories. The method is fully automated and requires no supervision. Furthermore, it enables the prediction of estimation error probability density, which is asymptotically Gaussian, for both classical and enhanced approaches. Leveraging this prediction, we introduce a specialized deconvolution algorithm to reconstruct the underlying particle ensemble structure from experimental data.