Speaker
Nilanjan Banerjee
(Fermi National Accelerator Laboratory)
Description
In non-linear optics, achieving integrability can enhance the dynamic aperture in storage rings. We analyze turn-by-turn phase-space data from our Danilov-Nagaitsev lattice implementation at Fermilab's Integrable Optics Test Accelerator using machine learning. AI Poincare estimates conserved quantities from experimental data without prior knowledge of the invariant structure, showing qualitative agreement with theoretical predictions. Additionally, one of the two learned invariants exhibits superior conservation compared to known theoretical expressions.
Funding Agency
This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics.
| Region represented | North America |
|---|---|
| Paper preparation format | LaTeX |
Primary author
Nilanjan Banerjee
(Fermi National Accelerator Laboratory)
Co-authors
Alexander Romanov
(Fermi National Accelerator Laboratory)
Alexander Valishev
(Fermi National Accelerator Laboratory)
Giulio Stancari
(Fermi National Accelerator Laboratory)
John Wieland
(Fermi National Accelerator Laboratory)
Nikita Kuklev
(Argonne National Laboratory)
