  We report on the automatic alignment of a transmission electron microscope
equipped with an orbital angular momentum sorter using a convolutional neural
network. The neural network is able to control all relevant parameters of both
the electron-optical setup of the microscope and the external voltage source of
the sorter without input from the user. It is able to compensate for mechanical
and optical misalignments of the sorter, in order to optimize its spectral
resolution. The alignment is completed over a few frames and can be kept stable
by making use of the fast fitting time of the neural network.
