ANL, Lemont, Illinois, USA
Pytomic is a new tool for the simulation and analysis of atomic beams through magnetic systems. It is written in Python and based on the same fundamentals as other particle tracking codes but for atomic beams instead of charged beams. In this case, the manipulation and control of neutral atomic beams is via a force due to the spin interacting with a magnetic field gradient. An object-oriented tool was developed to aid in the design of a beamline through the manipulation of modular elements. The Python language allowed for a smooth implementation and kept the code clear and simple. The primary purpose of developing this code was to have a tool to design, simulate, and optimize a Breit-Rabi Polarimeter to measure the polarization of an atomic beam. Therefore, different set-ups with different magnets need to be simulated and optimized for direct comparison. In addition to simulation and tracking modules, a new data analysis module was developed to be able to quickly analyze simulation results, gaining insight from each iteration of the simulation, leading to an efficient and rapid design process. Example applications to design polarimeters for atomic beams will be presented.
ANL, Lemont, Illinois, USA
ATLAS is a DOE/NP User Facility for the study of low-energy nuclear physics with heavy ions. It operates ~6000 hours per year. In addition to delivering any stable beam from proton to uranium, the facility also provides radioactive beams from the CARIBU source or via the in-flight radioactive ion separator, RAISOR. The facility uses 3 ion sources and services 6 target areas at energies from ~1-15 MeV/u. To accommodate the large number and variety of approved experiments, ATLAS reconfigures once or twice per week over 40 weeks of operation per year. The startup time varies from ~12-48 hours depending on the complexity of the tuning, which will increase with the upcoming Multi-User Upgrade to deliver beam to two experimental stations simultaneously. DOE/NP has recently approved a project to use AI/ML to support ATLAS operations. The project aim is to significantly reduce the accelerator tuning time and improve machine performance by developing and deploying artificial intelligence methods. These improvements will increase the scientific throughput of the facility and the quality of the data collected. Our recent developments and future plans will be presented and discussed.