Engineering topics

Nanni, Emilio

Publications and source records attributed to Nanni, Emilio.

3 records

DOE OSTI2025

Three-dimensional reconstruction of THz near-fields from a LiNbO3 optical rectification source

Terahertz (THz) generation by optical rectification in LiNbO3 (LN) is a widely used technique for generating intense THz radiation. The spatiotemporal characterization of THz pulses from these sources is currently limited to far-field methods. While simulations of tilted pulse front THz generation have been published, little work has been done to measure the near-field properties of the THz source. A better understanding of the THz near-field properties will improve optimization of THz generation efficiency, transport, and coupling. We demonstrate a technique for quantitative spatiotemporal characterization of single-cycle strong-field THz pulses with 2-D near-field electro-optic imaging. We have reconstructed the full temporal 3D THz near-field and shown how the phase front can be tailored by controlling the incident pump pulse.

Gabriel, Annika↗

DOE OSTI2024

An engineering prototype of a late stage ionization cooling cell for a muon collider

Achieving the low emittances necessary for a muon collider requires ionization cooling. Much of that cooling occurs in compact cooling cells where superconducting coils and conventional RF cavities are closely interleaved [1]. The real challenges for these cooling cells reside in their engineering challenges: high field solenoids, RF cavities, and absorbers, often designed near technological limits, placed in close proximity to each other. We thus propose to build a prototype ionization cooling cell to demonstrate the capability of constructing an ionization cooling channel reaching the lowest emittances and to provide engineering input for the design of such beamlines. The magnets and cavities will be powered at their design values, and an absorber will be included along with a mechanism for heating the absorber similarly to how a beam would.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Multi-objective Bayesian active learning for MeV-ultrafast electron diffraction

Ultrafast electron diffraction using MeV energy beams(MeV-UED) has enabled unprecedented scientific opportunities in the study of ultrafast structural dynamics in a variety of gas, liquid and solid state systems. Broad scientific applications usually pose different requirements for electron probe properties. Due to the complex, nonlinear and correlated nature of accelerator systems, electron beam property optimization is a time-taking process and often relies on extensive hand-tuning by experienced human operators. Algorithm based efficient online tuning strategies are highly desired. Here, we demonstrate multi-objective Bayesian active learning for speeding up online beam tuning at the SLAC MeV-UED facility. The multi-objective Bayesian optimization algorithm was used for efficiently searching the parameter space and mapping out the Pareto Fronts which give the trade-offs between key beam properties. Such scheme enables an unprecedented overview of the global behavior of the experimental system and takes a significantly smaller number of measurements compared with traditional methods such as a grid scan. This methodology can be applied in other experimental scenarios that require simultaneously optimizing multiple objectives by explorations in high dimensional, nonlinear and correlated systems.

43 PARTICLE ACCELERATORS↗