DOE OSTI · 2376951
Experimental Verification of Integrability in a Danilov-Nagaitsev Lattice using Machine Learning
Abstract
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. \textit{AI Poincar\'e} 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.
Explore related subjects
Keep this discovery
Explore connections, maps & timelines
Banerjee, Nilanjan. 2024-06-17. Experimental Verification of Integrability in a Danilov-Nagaitsev Lattice using Machine Learning. https://doi.org/10.2172/2376951
Cite the original work for its findings. Save a collection to share your selection of sources.