Speaker
Description
The isolated resonances model of spin depolarization hinges on a closed-form solution of energy ramping through the Single Resonance Model by Froissart and Stora. However, depolarization by single-resonance crossing is impossible in the SRM with pairs of Siberian Snakes for appropriately chosen orbital tunes, since the amplitude-dependent spin tune is fixed to one-half. This demonstrates that the isolated resonances model is not a good approximation of polarization dynamics with Siberian Snakes. We therefore develop an extended model in which pairs of resonances in close proximity push the amplitude-dependent spin tune away from one-half in the presence of Siberian Snakes, allowing the crossing of higher-order spin resonances associated with depolarization. Here we present results from applying Machine Learning methods that establish spin transport models with two overlapping resonances from tracking data. These can then be used to predict spin tune deviations from ½ over a large parameter space, allowing us to find conditions for the presence or avoidance of higher-order resonances, and to compute the effective strengths of such higher-order resonances.
Funding Agency
Work supported by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy, No. DE SC-0024287, and DE-SC0018008.
| Region represented | North America |
|---|---|
| Paper preparation format | LaTeX |
