  Distinguishing two objects or point sources located closer than the Rayleigh
distance is impossible in conventional microscopy. Understandably, the task
becomes increasingly harder with a growing number of particles placed in close
proximity. It has been recently demonstrated that subwavelength nanoparticles
in closely packed clusters can be counted by AI-enabled analysis of the
diffraction patterns of coherent light scattered by the cluster. Here we show
that deep learning analysis can determine the actual position of the
nanoparticle in the cluster of subwavelength particles from a sing-shot
diffraction pattern even if they are separated by distances below the Rayleigh
resolution limit of a conventional microscope.
