BNL, Upton, New York, USA
UNM-ECE, Albuquerque, USA
SBU, Stony Brook, USA
Stony Brook University, Stony Brook, New York, USA
LBNL, Berkeley, USA
Particle accelerators are among the largest, most complex devices. To meet the challenges of increasing energy, intensity, accuracy, compactness, complexity and efficiency, increasingly sophisticated computational tools are required for their design and optimization. It is key that contemporary software take advantage of the latest advances in computer hardware and scientific software engineering practices, delivering speed, reproducibility and feature composability for the aforementioned challenges. A new open source software stack is being developed at the heart of the Beam pLasma Accelerator Simulation Toolkit (BLAST) by LBNL and collaborators, providing new particle-in-cell modeling codes capable of exploiting the power of GPUs on Exascale supercomputers. Combined with advanced numerical techniques, such as mesh-refinement, and intrinsic support for machine learning, these codes are primed to provide ultrafast to ultraprecise modeling for future accelerator design and operations.
[1] J.-L. Vay, A. Huebl, et al, Phys. Plasmas 28, 023105 (2021)
[2] J.-L. Vay, A. Huebl, et al, J. Instr. 16, T10003 (2021)
[3] A. Myers, et al (incl. A. Huebl), Parallel Comput. 108, 102833 (2021)
Euclid TechLabs, Solon, Ohio, USA
AJS Smartech LLC, Naperville, TX, USA
The ever-expanding size of accelerator operation and experimental data including those generated by electron microscopes and beamline facilities renders most proprietary software inefficient at managing data. The Findability, Accessibility, Interoperability, and Reuse (FAIR) principles of digital assets require a convenient platform for users to share and manage data on. An open-source data framework for storing raw data and metadata, hosting databases, and providing a platform for data processing and visualization is highly desirable. In this paper, we present an open-source, infrastructure-independent data management software framework, named by Euclid-NexusLIMS, to archive, register, record, visualize and process experimental data. The software was targeted initially for electron microscopes, but can be widely applied to all scientific experimental data.
ANL, Lemont, Illinois, USA
We explore the possibility of using a Machine Learning (ML) algorithm to identify the source of occasional poor performance of the Particle Accumulator Ring (PAR) and the Linac-To-PAR (LTP) transport line, which are parts of the injector complex of the Advanced Photon Source (APS) at Argonne National Lab. The cause of reduced injection or extraction efficiencies may be as simple as one parameter being out of range. Still, it may take an expert considerable time to notice it, whereas a well-trained ML model can point at it instantly. In addition, a machine expert might not be immediately available when a problem occurs. Therefore, we began by focusing on such single-parameter anomalies. The training data were generated by creating controlled perturbations of several parameters of PAR and LTP one-by-one, while continuously logging all available process variables. Then, several ML classifiers were trained to recognize certain signatures in the logged data and link them to the sources of poor machine performance. Possible applications of autoencoders and variational autoencoders for unsupervised anomaly detection and for anomaly clustering were considered as well.
TUYE5
SLAC, Menlo Park, California, USA
ANL, Lemont, Illinois, USA
Stanford University, Stanford, California, USA
TUYE6
MIT, Cambridge, Massachusetts, USA
PSI, Villigen PSI, Switzerland
Computation has become a critically important tool for particle accelerator design and optimization. Thanks to massively parallel codes running on high-performance clusters, we can now accurately predict emergent properties of particle ensembles and non-linear collective effects, and use machine learning (ML) for analysis and to create "virtual twins" of accelerator systems. Here, we will present the IsoDAR experiment in neutrino physics as an example. For it, we have developed a compact and cost-effective cyclotron-based driver to produce very high-intensity beams. The system will be able to deliver cw proton currents of 10 mA on target in the energy regime around 60 MeV. 10 mA is a factor of 10 higher than commercially available machines. This increase in current is possible due to longitudinal-radial coupling through space charge, an effect dubbed "vortex motion". We will discuss the high-fidelity OPAL simulations performed to simulate this effect in the IsoDAR cyclotron and predict beam losses due to halo formation. We will present uncertainty quantification for this design and we will show our study to optimize the IsoDAR injector RFQ using ML.