Understanding Spin-Tune Variations at COSY

In the project "Understanding Spin-Tune Variations at COSY" we help understanding interesting measurements of the "Spin Tune" with Machine Learning methods at the particle accelerator COSY in Jülich.

The COoler SYnchrtron COSY is a particle accelerator at the Institute for Nuclear Physics at Reserach Center Jülich. It generates proton beams in the energy range between 45 and 2700 MeV or deuteron beams between 90 and 2100 MeV for fundamental science experiments. COSY can create spin-polarized beams, where the spin axis of all particles are aligned. The quantity spin tune is defined as the number of spin precessions per turn in the accelerator. Under ideal conditions the spin tune gives a simple relation for the particle speed and a fundamental property of the particle and can be measured with high accuracy at COSY [1].
However, in recent measurements it was apparent that the spin tune does not obey the simple relationship, but deviates slightly as the measurement conditions deviated from ideal conditions. Measurements show that this deviation has a complex dependency on many other measured quantities and can depend on the operation mode of the accelerator.

In this project, our team applies machine learning methods to contribute to the understanding of these phenomena. We apply ML pipelines based on dimensionality reduction, regularized linear regressions, and Echo State Networks [2]. A key challenge is that the number of experimental data points is very limited, and therefore selecting training and test sets must be done very carefully and regularization is a top priority. In the image shown below, a preliminary result from a multilinear regresssion model is shown.

Forschungszentrum Jülich

Collaborators: Jörg Pretz (FZ Jülich)

  1. Eversmann, D., et al. "New method for a continuous determination of the spin tune in storage rings and implications for precision experiments." Physical review letters 115.9 (2015): 094801.
  2. Jaeger, Herbert, and Harald Haas. "Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication." science 304.5667 (2004): 78-80.
Last Modified: 30.08.2023