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.

The Fermilab Accelerator Science and Technology (FAST) facility is dedicated to the exploration of novel concepts in accelerator and beam physics, and the development of a robust workforce, in order to enable and enhance next-generation particle accelerators. FAST comprises a high-brightness superconducting electron linac, and a storage ring, the Integrable Optics Test Accelerator (IOTA). Experiments in the most recent operational run include studies of nonlinear integrable lattices; tracking of single electrons; precise characterization of undulator radiation; studies with low-momentum-compaction lattices; and ultra-wide range beam diagnostics based on Photomultiplier tubes. In the linac, experiments on noise in intense electron bunches were conducted. The IOTA proton injector, currently being commissioned, will enable a diverse program on space-charge-dominated beams. Research areas include non-invasive beam profile monitoring for proton beams; beam dynamics with electron lenses; halo suppression, feedback systems, and electron cooling. In this presentation, we provide an overview of the recent results and highlight future plans together with opportunities for collaboration.

The Advanced Photon Source (APS) facility has just completed an upgrade to become one of the world’s brightest storage-ring light sources. For the first time, machine learning (ML) methods have been developed and used as part of the baseline commissioning plan. One such method is Bayesian optimization (BO) – a versatile tool for efficient high-dimensional single and multi-objective tuning, as well as surrogate model construction and other purposes. In this paper we will present our development work on adapting BO to practical control room problems such as tuning linac and booster transmission efficiency, injection stabilization, enlarging storage ring dynamic and momentum apertures, and various other tasks. We will also show first experimental results of these efforts, including achieving initial beam capture in the APS-U storage ring. Given the success of BO methods at APS, we are working on tighter ML method integration into the standard control room procedures through a dedicated graphical interface.

Nonlinear integrable optics are a promising alternative approach to lattice design. The integrable optics test accelerator (IOTA) at Fermilab has been constructed for dedicated studies of magnetostatic elliptical elements as described by Danilov and Nagaitsev. The most compelling verification of correct implementation of the NIO lattice is direct observation of the analytically expected invariants. This report outlines the experimental and analytical methods for extracting the nonlinear invariants of motion from data gathered in the last IOTA run.

One of the most promising advantages of nonlinear integrable optics is strong amplitude dependent tune shift without degrading the dynamic aperture. The integrable optics test accelerator (IOTA) at Fermilab is constructed around nonlinear lattice elements of the elliptical type as described by Danilov and Nagaitsev. Detuning and dynamic aperture scans in IOTA were performed using a fast dipole kicker and a low emittance electron beam. The evolution of the dynamic aperture and detuning for different configurations of the integrable optics lattice are presented.