IPAC'24 - 15th International Particle Accelerator Conference

Experimental verification of integrability in a Danilov-Nagaitsev lattice using machine learning

MOPS67

20 May 2024, 16:00

2h

Blues (MCC Exhibit Hall A)

Poster Presentation MC5.D13 Machine Learning Monday Poster Session

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 Poincaré 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 comparable or better 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.