  We seek to characterize the motility of mouse fibroblasts on 2D substrates.
Utilizing automated tracking techniques, we find that cell trajectories are
super-diffusive, where displacements scale faster than t^(1/2) in all
directions. Two mechanisms have been proposed to explain such statistics in
other cell types: run and tumble behavior with L\'{e}vy-distributed run times,
and ensembles of cells with heterogeneous speed and rotational noise. We
develop an automated toolkit that directly compares cell trajectories to the
predictions of each model and demonstrate that ensemble-averaged quantities
such as the mean-squared displacements and velocity autocorrelation functions
are equally well-fit by either model. However, neither model correctly captures
the short-timescale behavior quantified by the displacement probability
distribution or the turning angle distribution. We develop a hybrid model that
includes both run and tumble behavior and heterogeneous noise during the runs,
which correctly matches the short-timescale behaviors and indicates that the
run times are not L\'{e}vy distributed. The analysis tools developed here
should be broadly useful for distinguishing between mechanisms for
superdiffusivity in other cells types and environments.
