Zobrazeno 1 - 10
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pro vyhledávání: '"A. Scheinker"'
Autor:
F. Cropp, L. Moos, A. Scheinker, A. Gilardi, D. Wang, S. Paiagua, C. Serrano, P. Musumeci, D. Filippetto
Publikováno v:
Physical Review Accelerators and Beams, Vol 26, Iss 5, p 052801 (2023)
In this work, nondestructive virtual diagnostics are applied to retrieve the electron beam time of arrival and energy in a relativistic ultrafast electron diffraction (UED) beamline using independently measured machine parameters. This technique has
Externí odkaz:
https://doaj.org/article/c87a64586a024717a6267645966b792d
Charged particle dynamics under the influence of electromagnetic fields is a challenging spatiotemporal problem. Many high performance physics-based simulators for predicting behavior in a charged particle beam are computationally expensive, limiting
Externí odkaz:
http://arxiv.org/abs/2408.07847
Autor:
Scheinker, Alexander
Imaging the 6D phase space of a beam in a particle accelerator in a single shot is currently impossible. Single shot beam measurements only exist for certain 2D beam projections and these methods are destructive. A virtual diagnostic that can generat
Externí odkaz:
http://arxiv.org/abs/2407.20218
Autor:
Scheinker, Alexander
Advanced accelerator-based light sources such as free electron lasers (FEL) accelerate highly relativistic electron beams to generate incredibly short (10s of femtoseconds) coherent flashes of light for dynamic imaging, whose brightness exceeds that
Externí odkaz:
http://arxiv.org/abs/2407.10693
We develop a remote patient monitoring (RPM) service architecture, which has two tiers of monitoring: ordinary and intensive. The patient's health state improves or worsens in each time period according to certain probabilities, which depend on the m
Externí odkaz:
http://arxiv.org/abs/2406.18000
Addressing the charged particle beam diagnostics in accelerators poses a formidable challenge, demanding high-fidelity simulations in limited computational time. Machine learning (ML) based surrogate models have emerged as a promising tool for non-in
Externí odkaz:
http://arxiv.org/abs/2406.01535
Particle accelerators are time-varying systems whose components are perturbed by external disturbances. Tuning accelerators can be a time-consuming process involving manual adjustment of multiple components, such as RF cavities, to minimize beam loss
Externí odkaz:
http://arxiv.org/abs/2406.01532
We present Assignably Safe Extremum Seeking (ASfES), an algorithm designed to minimize a measured objective function while maintaining a measured metric of safety (a control barrier function or CBF) be positive in a practical sense. We ensure that fo
Externí odkaz:
http://arxiv.org/abs/2404.08842
Particle 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 simulatio
Externí odkaz:
http://arxiv.org/abs/2403.13858
We introduce a type of safe extremum seeking (ES) controller, which minimizes an unknown objective function while also maintaining practical positivity of an unknown barrier function. We show semi-global practical asymptotic stability of our algorith
Externí odkaz:
http://arxiv.org/abs/2309.15401