Beam loss is a critical challenge in the physics design of high power superconducting proton linacs. The challenge is even more acute in linacs that feature high peak intensity and low energy, which has strong space charge effect and RF nonlinear force. In this paper, we study how to achieve a high transmission rate for beam halo particles, commonly a major source of beam loss, via beam halo matching and acceptance optimization. We employ this method of beam loss reduction to improve the physics design of a high power 100mA superconducting linac which has potential applications in high brightness neutron production.

Chopper systems are typically used to provide beam time structure and ensure the safety of accelerator operations by deflecting the beam away. To meet the strict beam stopping time requirements of the China Initiative Accelerator Driven System (CiADS), an E × B chopper design has been developed based on a permanent magnet and an electrostatic deflection plate. In this paper, the physical design of the chopper system is detailed, and each component of the chopper is optimized through simulation analysis. Finally, the feasibility of the chopper system is validated through multi-particle simulations and error analysis.

High reliability is a major challenge of high-current linear accelerators. This is particularly problematic for Accelerator Driven Systems(ADS) such as the China initiative Accelerator Driven System(CiADS). In order to achieve rapid beam recovery, it is necessary to adjust and compensate the cavities adjacent to the failed cavity. In this study, we employ the Proximal Policy Optimization (PPO) algorithm, a reinforcement learning technique, to train a compensation model within a simulated environment of the CiADS superconducting HWR010 section. Compared to previous methods utilizing genetic algorithms, the reinforcement learning approach demonstrates superior performance in delivering more stable and consistent results.

Chopper systems are typically used to provide beam time structure and ensure the safety of accelerator operations by deflecting the beam away. To meet the strict beam stopping time requirements of the China initiative Accelerator Driven System (CiADS), an E × B chopper design has been developed based on a permanent magnet and an electrostatic deflection plate. In this paper, the physical design of the chopper system is detailed, and each component of the chopper is optimized through simulation analysis. Finally, the feasibility of the chopper system is validated through multi-particle simulations and error analysis.

China Accelerator Facility for Superheavy Elements (CAFE2) is a state-of-the-art scientific facility dedicated to the synthesis and investigation of superheavy nuclei and elements. It features a continuous wave RFQ, a fully superconducting linac that comprises twenty three half-wave resonator cavities housed within four cryostats, and an experimental terminal. The linac is designed to achieve an energy of 6.5 MeV/u for ion species with an A/Q ratio approximately equal to 3.5. CAFE2 evolved from its predecessor, CAFe, which was initially a proton demo linac for accelerator driven systems. Following its upgrade in 2021, CAFE2 has accumulated over 10000 hours of operation time, providing heavy ion beams for superheavy element experiments at beam currents exceeding 10 upA. This talk will review the operation of CAFE2 and highlight the efforts to enhance the stability and tuning efficiency of high-intensity heavy ion beams.

High reliability is a major challenge of high-current linear accelerators. This is particularly problematic for Accelerator Driven Systems(ADS) such as the China initiative Accelerator Driven System(CiADS). In order to achieve rapid beam recovery, it is necessary to adjust and compensate the cavities adjacent to the failed cavity. In this study, we employ the Proximal Policy Optimization (PPO) algorithm, a reinforcement learning technique, to train a compensation model within a simulated environment of the CiADS superconducting HWR010 section. Compared to previous methods utilizing genetic algorithms, the reinforcement learning approach demonstrates superior performance in delivering more stable and consistent results.

Beam loss is a critical challenge in the physics design of high power superconducting proton linacs. The challenge is even more acute in linacs that feature high peak intensity and low energy, which has strong space charge effect and RF nonlinear force. In this paper, we study how to achieve a high transmission rate for beam halo particles, commonly a major source of beam loss, via beam halo matching and acceptance optimization. We employ this method of beam loss reduction to improve the physics design of a high power 100 mA superconducting linac which has potential applications in high brightness neutron production.

The Superconducting Ion Linac (iLinac) of High Intensity Heavy-ion Accelerator Facility (HIAF) is composed of an ECR ion source, low energy beam transport line (LEBT), 81.25MHz radio frequency quadrupole (RFQ) accelerator, medium energy beam transport line (MEBT) and superconducting section composed by Quarter Wave Resonators (QWR007) and Half Wave Resonators (HWR015). The 238U35+ beam is accelerated by the RFQ accelerator from 0.014 MeV/u to 0.8 MeV/u and then to 17 MeV/u by 96th SC cavities. The room-temperature front end of the iLINAC has been assembled and successfully produced a beam at present. Three cryostats have been installed in the tunnel.