Time series forecasting methods and their applications to particle accelerators [PDF]
Physical Review Accelerators and Beams, 2023 Particle accelerators are complex facilities that produce large amounts of structured data and have clear optimization goals as well as precisely defined control requirements. As such they are naturally amenable to data-driven research methodologies. The
Sichen Li, Andreas Adelmann
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Turn-key constrained parameter space exploration for particle accelerators using Bayesian active learning. [PDF]
Nat Commun, 2021 Particle accelerators are invaluable discovery engines in the chemical, biological and physical sciences. Characterization of the accelerated beam response to accelerator input parameters is often the first step when conducting accelerator-based ...
Roussel R +8 more
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An adaptive approach to machine learning for compact particle accelerators. [PDF]
Sci Rep, 2021 Machine learning (ML) tools are able to learn relationships between the inputs and outputs of large complex systems directly from data. However, for time-varying systems, the predictive capabilities of ML tools degrade if the systems are no longer ...
Scheinker A +3 more
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A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators. [PDF]
Sci RepParticle accelerators are complex systems that focus, guide, and accelerate intense charged particle beams to high energy. Beam diagnostics present a challenging problem due to limited non-destructive measurements, computationally demanding simulations ...
Rautela M, Williams A, Scheinker A.
europepmc +3 more sources
Biomedical Research Programs at Present and Future High-Energy Particle Accelerators. [PDF]
Front Phys, 2020 Biomedical applications at high-energy particle accelerators have always been an important section of the applied nuclear physics research. Several new facilities are now under constructions or undergoing major upgrades.
Patera V +19 more
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Uncertainty quantification for virtual diagnostic of particle accelerators [PDF]
Physical Review Accelerators and Beams, 2021 Virtual diagnostic (VD) is a computational tool based on deep learning that can be used to predict a diagnostic output. VDs are especially useful in systems where measuring the output is invasive, limited, costly or runs the risk of altering the output ...
Owen Convery +3 more
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Machine learning-based longitudinal phase space prediction of particle accelerators
Physical Review Accelerators and Beams, 2018 We report on the application of machine learning (ML) methods for predicting the longitudinal phase space (LPS) distribution of particle accelerators.
C. Emma +5 more
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Robust errant beam prognostics with conditional modeling for particle accelerators [PDF]
Machine Learning: Science and Technology, 2023 Particle accelerators are complex and comprise thousands of components, with many pieces of equipment running at their peak power. Consequently, they can fault and abort operations for numerous reasons, lowering efficiency and science output.
Kishansingh Rajput +7 more
semanticscholar +1 more source
Next Generation Computational Tools for the Modeling and Design of Particle Accelerators at Exascale [PDF]
arXiv.org, 2022 Particle accelerators are among the largest, most complex devices. To meet the challenges of increasing energy, intensity, accuracy, compactness, complexity and ef-ficiency, increasingly sophisticated computational tools are required for their design and ...
A. Huebl +6 more
semanticscholar +1 more source
Tuning Particle Accelerators with Safety Constraints using Bayesian Optimization [PDF]
Physical Review Accelerators and Beams, 2022 Tuning machine parameters of particle accelerators is a repetitive and time-consuming task that is challenging to automate. While many off-the-shelf optimization algorithms are available, in practice their use is limited because most methods do not ...
Johannes Kirschner +5 more
semanticscholar +1 more source