Deep learning for robust orbit control of Elettra 2.0 storage ring.

Area of Science:

• Accelerator Physics
• Machine Learning Applications
• Particle Beam Dynamics

Background:

• Advancements in light source accelerators require high beam quality.
• Precise control of particle orbits is crucial for stable, high-quality beams.
• Automatic orbit control is needed to correct beam position errors.

Purpose of the Study:

• To propose a computational framework for automatic electron beam orbit control.
• To utilize deep learning for predicting corrector magnetic field strengths.
• To enhance beam stability in the Elettra 2.0 storage ring design.

Main Methods:

• A deep convolutional neural network was employed to predict corrector magnetic field strengths.
• Transfer learning was used to sequentially train system and controller deep learning models.
• The method was evaluated using simulations for the Elettra 2.0 storage ring lattice.

Main Results:

• The deep learning approach demonstrated effectiveness and efficiency in orbit control.
• The method showed robust generalizability for real-world accelerator applications.
• Achieved 7% greater stability suppression compared to singular value decomposition on the Elettra 2.0 ring.

Conclusions:

• The proposed computational framework successfully enables automatic electron beam orbit control.
• This machine learning approach offers significant improvements in beam stability for accelerators.
• The framework is adaptable for use in other synchrotron facilities.