  Interferometric scattering microscopy (iSCAT) can image the dynamics of
nanometer-scale systems. The typical approach to analyzing interferometric
images involves intensive processing, which discards data and limits the
precision of measurements. We demonstrate an alternative approach: modeling the
interferometric point spread function (iPSF) and fitting this model to data
within a Bayesian framework. This approach yields best-fit parameters,
including the particle's three-dimensional position and polarizability, as well
as uncertainties and correlations between these parameters. Building on recent
work, we develop a model that is parameterized for rapid fitting. The model is
designed to work with Hamiltonian Monte Carlo techniques that leverage
automatic differentiation. We validate this approach by fitting the model to
interferometric images of colloidal nanoparticles. We apply the method to track
a diffusing particle in three dimensions, to directly infer the diffusion
coefficient of a nanoparticle without calculating a mean-square displacement,
and to quantify the ejection of DNA from an individual lambda phage virus,
demonstrating that the approach can be used to infer both static and dynamic
properties of nanoscale systems.
