Bayesian optimization scheme for the design of a nanofibrous high power target

TL;DR

This paper applies Bayesian optimization to efficiently design nanofibrous high power targets, balancing target survival and secondary particle yield for accelerator applications.

Contribution

It introduces a Bayesian optimization framework tailored for nanofiber target design, integrating simulation-based evaluation of construction parameters.

Findings

Optimal fiber packing density identified for target durability.

Bayesian optimization effectively navigates the design space.

Framework facilitates future design improvements.

Abstract

High Power Targetry (HPT) R&D is critical in the context of increasing beam intensity and energy for next generation accelerators. Many target concepts and novel materials are being developed and tested for their ability to withstand extreme beam environments; the HPT R&D Group at Fermilab is developing an electrospun nanofiber material for this purpose. The performance of these nanofiber targets is sensitive to their construction parameters, such as the packing density of the fibers. Lowering the density improves the survival of the target, but reduces the secondary particle yield. Optimizing the lifetime and production efficiency of the target poses an interesting design problem, and in this paper we study the applicability of Bayesian optimization to its solution. We first describe how to encode the nanofiber target design problem as the optimization of an objective function, and how to evaluate that function with computer simulations. We then explain the optimization loop setup. Thereafter, we present the optimal design parameters suggested by the algorithm, and close with discussions of limitations and future refinements.