Online regularization of Poincar\'e map of storage rings with Shannon entropy

TL;DR

This paper introduces a novel method using Shannon entropy to quantify chaos in Poincaré maps for real-time optimization of storage ring nonlinearities, enhancing the understanding and control of accelerator dynamics.

Contribution

It is the first to apply Shannon entropy as a measurable chaos indicator for online nonlinear optimization in a real-world particle accelerator.

Findings

Shannon entropy effectively quantifies chaos in Poincaré maps.

Canonical transformations enable extraction of nonlinear characteristics.

Entropy-based optimization improves dynamic aperture in NSLS-II.

Abstract

Shannon entropy, as a chaos indicator, is used for online Poincar\'e map regularization and dynamic aperture optimization in the National Synchrotron Light Source-II (NSLS-II) ring. Although various chaos indicators are widely used in studying nonlinear dynamical systems, including modern particle accelerators, it is the first time to use a measurable one in a real-world machine for online nonlinear optimization. Poincar\'e maps, constructed with the turn-by-turn beam trajectory readings from beam position monitors, are commonly used to observe the nonlinearity in ring-based accelerators. However, such observations typically only provide a qualitative interpretation. We analyze their entropy to quantify the chaos in measured Poincar\'e maps. After some canonical transformations on the Poincar\'e maps, not only can the commonly used nonlinear characterizations be extracted, but more importantly, the chaos can be quantitatively calibrated with Shannon entropy, and then used as the online optimization objectives.