Neural Networks for ID Gap Orbit Distortion Compensation in PETRA III

TL;DR

This paper explores the use of deep neural networks to predict and compensate for orbit distortions caused by undulator gap variations in PETRA III, aiming to improve beam stability in synchrotron radiation facilities.

Contribution

It compares various state-of-the-art deep learning architectures to identify the most effective model for undulator gap-induced orbit distortion compensation.

Findings

Deep neural networks can effectively predict orbit distortions.

Certain architectures outperform others in accuracy.

The best model improves orbit stability during gap changes.

Abstract

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 current 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.