High Power Cyclotrons: The Bridge Between Beyond the Standard Model Physics, Computation, and Medical Applications

TL;DR

This paper discusses the IsoDAR high power cyclotron's capabilities in advancing beyond Standard Model physics, medical isotope production, and computational design innovations, including machine learning applications for RFQ optimization.

Contribution

It introduces novel high-current H₂⁺ beam production, a versatile beam splitting technique, and pioneering machine learning methods for RFQ design in cyclotrons.

Findings

Achieved record high purity, low emittance H₂⁺ current.

Designed a novel RFQ with machine learning optimization.

Proposed high-rate production of medical isotopes like Ac-225.

Abstract

The IsoDAR cyclotron is a 60 MeV cyclotron designed to output 10mA of protons in order to be a driver for a neutrino experiment. Coupling the high flux generated by the IsoDAR system with a kiloton neutrino detector will provide sterile neutrino exclusion searches covering anomalous regions indicated by short baseline experiments. Simultaneously, the coupling of a high power target and kiloton detector allows for the investigation of dark matter candidates, namely axion-like particles. We have shown that nuclear excitations within the IsoDAR target create a unique opportunity to produce axions and detect monoenergetic peaks with the nearby kiloton detector. Beyond this, the high power produced by the IsoDAR cyclotron can be used for applications beyond particle physics. The IsoDAR cyclotron accelerates and extracts H$_2^+$, which allows the beam to be split downstream, a versatile and important development to alleviate the problem of producing high-power targets for the medical isotope community. This thesis presents a proposal for production of an more than an order of magnitude higher rates than are available at present for certain highly-need medical isotopes, including Ac-225. In this thesis, we report results from a multi-cusp ion source that meets these requirements and produces a record level of high purity, low emittance H$_2^+$ current. The second has been to design a radio-frequency quadrupole (RFQ) that will allow for gentle bunching of the high current before injection. This is the first use of axial direct injection with a compact cyclotron. This thesis reports the first application of machine learning to the RFQ design. These tools enable the high currents required by IsoDAR cyclotron, leading to important impact on the accelerator, medical, and physics communities.