Speaker
Description
Determining the conditions under which reduced-order simulation models faithfully capture collective effects is a challenge in the beam dynamics modeling. The parameter spaces involved: beam energy, transverse and longitudinal emittance, current profiles, lattice elements and associated vacuum chamber geometry etc, often manifest themselves in highly nonlinear and unintuitive ways. While simple heuristics like the Derbenev condition for coherent synchrotron radiation (CSR) exist, generalizations to more complex lattice elements and beam structures necessitate expensive simulation sweeps over a complicated parameter search-space. To that end, we present an agentic harness in which a large language model autonomously orchestrates a parameter study comparing 1D and 3D CSR models over a range of beam and lattice parameters. The agent is equipped with tools to propose parameter combinations, dispatch and monitor simulations, accumulate results into a running dataset, which can later be processed to derive physical validity bounds. This AI-assisted strategy autonomously concentrates computational effort in areas where the physics is richest, potentially improving on algorithmic parameter sweeps. The results of this parameter study are used to generalize the Derbenev criterion to account for shielding as well as a wider range of beam current profiles and transverse shapes.
This research was supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics under Award DE-SC0024445.
| Working group | WG5 |
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