  Current quantum simulation experiments are starting to explore
non-equilibrium many-body dynamics in previously inaccessible regimes in terms
of system sizes and time scales. Therefore, the question emerges which
observables are best suited to study the dynamics in such quantum many-body
systems. Using machine learning techniques, we investigate the dynamics and in
particular the thermalization behavior of an interacting quantum system which
undergoes a dynamical phase transition from an ergodic to a many-body localized
phase. A neural network is trained to distinguish non-equilibrium from thermal
equilibrium data, and the network performance serves as a probe for the
thermalization behavior of the system. We test our methods with experimental
snapshots of ultracold atoms taken with a quantum gas microscope. Our results
provide a path to analyze highly-entangled large-scale quantum states for
system sizes where numerical calculations of conventional observables become
challenging.
