UMD, College Park, Maryland, USA
We present the numerical optimization, using adjoint techniques, of Flat-to-Round (FTR) transformers operating in the strong self-field limit. FTRs transform an unmagnetized beam that has a high aspect ratio, elliptical spatial cross section, to a round beam in a solenoidal magnetic field. In its simplest form the flat to round conversion is accomplished with a triplet of quadrupoles, and a solenoid. FTR transformers have multiple applications in beam physics research, including manipulating electron beams to cool co-propagating hadron beams. Parameters that can be varied to optimize the FTR conversion are the positions and strengths of the four magnet elements, including the orientations and axial profiles of the quadrupoles and the axial profile and strength of the solenoid’s magnetic field. The adjoint method we employ [1] allows for optimization of the lattice with a minimum computational effort including self-fields. The present model is based on a moment description of the beam. However, the generalization to a particle description will be presented. The optimized designs presented here will be tested in experiments under construction at the University of Maryland.
[1] Optimization of Flat to Round Transformers with self-fields using adjoint techniques, L. Dovlatyan, B. Beaudoin, S. Bernal, I. Haber, D. Sutter and TMA, PhysRevAccelBeams.25.044002 (2022).
UMD, College Park, Maryland, USA
One of the DOE-HEP Grand Challenges identified by Nagaitsev et al. relates to the use of virtual particle accelerators for beam prediction and optimization. Useful virtual accelerators rely on efficient and effective methodologies grounded in theory, simulation, and experiment. This paper uses an algorithm called Sparse Identification of Nonlinear Dynamical systems (SINDy), which has not previously been applied to beam physics. We believe the SINDy methodology promises to simplify the optimization of accelerator design and commissioning, particularly where space charge is important. We show how SINDy can be used to discover and identify the underlying differential equation system governing the beam moment evolution. We compare discovered differential equations to theoretical predictions and results from the PIC code WARP modeling. We then integrate the discovered differential system forward in time and compare the results to data analyzed in prior work using a Machine Learning paradigm called Reservoir Computing. Finally, we propose extending our methodology, SINDy for Virtual Accelerators (SINDyVA), to the broader community’s computational and real experiments.
