Neural network reconstruction of longitudinal beam phase space from the synchrotron radiation spectrum

In order to control a charged particle beam and improve the quality of the system detailed information of the phase space of the charged particle beam system is needed. Coherent synchrotron radiation combined with a neural network as a tool can be used to determine the longitudinal phase space structure of a beam. In the case of coherent synchrotron radiation emission there are two regions in the spectrum. At the high frequency end the spectrum is just that expected from normal synchrotron radiation. At the low frequency end the spectrum is influenced by the beam structure in configuration space. A neural network can be used to solve the inverse problem of obtaining the distribution of particles which produces the radiation.