Developed Reinforced Learning Method to Optimize Polarization in Nuclear Physics

It is a complex tuning process that dynamically polarized targets are carried out in nuclear physics experiments, traditionally specialized operators with manual experimentation. To automate this process, scientists developed a new control system that combines **tax learning and data modeling techniques.

Basic features of the new approach:

• Automates continuous micro-wave frequency adjustment due to the changing properties of radiation damage and material
• Accelerates learning process by making quick predictions about the environment with proxy modeling (surrogate modeling)
• It can dynamically find optimal frequency values using reinforced learning algorithms
• It offers an applicable solution using experimental data directly in system training

This data-oriented control frame ensures consistent protection of target polarization quality in nuclear physics research. By reducing the need for human intervention, experiments can be carried out more efficiently and reliably.

Research shows the potential of artificial intelligence and machine learning in control of complex experimental systems. Such applications are promising not only in nuclear physics, but in other scientific fields that require similar sensitive adjustments.