  Light beams carrying orbital-angular-momentum (OAM) play an important role in
optical manipulation and communication owing to their unbounded state space.
However, it is still challenging to efficiently discriminate OAM modes with
large topological charges and thus only a small part of the OAM states have
been usually used. Here we demonstrate that neural networks can be trained to
sort OAM modes with large topological charges and unknown superpositions. Using
intensity images of OAM modes generalized in simulations and experiments as the
input data, we illustrate that our neural network has great generalization
power to recognize OAM modes of large topological charges beyond training areas
with high accuracy. Moreover, the trained neural network can correctly classify
and predict arbitrary superpositions of two OAM modes with random topological
charges. Our machine learning approach only requires a small portion of
experimental samples and significantly reduces the cost in experiments, which
paves the way to study the OAM physics and increase the state space of OAM
beams in practical applications.
