Neural networks for ID gap orbit distortion compensation in PETRA III

Undulators are used in storage rings to produce extremely brilliant synchrotron radiation. In the ideal case, a perfectly tuned undulator always has a first and second field integrals equal to zero. But, in practice, field integral changes during gap movements can never be avoided for real-life devices. As they significantly impact the circulating electron beam, there is the need to routinely compensate such effects. Deep Neural Networks can be used to predict the distortion in the closed orbit induced by the undulator gap variations on the circulating electron beam. In this contribution several state-of-the-art deep learning algorithms were trained on measurements from PETRA III. The different architecture performances are then compared to identify the best model for the gap-induced distortion compensation. It is found that realistic data, with several gaps moving simultaneously to arbitrary gaps must be used to train the neural networks. The deep feed-forward neural network was found to be more effective than the recurrent and convolutional neural networks.