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pro vyhledávání: '"Alexander Scheinker"'
Autor:
Alexander Scheinker
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract 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 exce
Externí odkaz:
https://doaj.org/article/61b9f6e144014a1fb554ee15cb0d5986
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-19 (2024)
Abstract 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
Externí odkaz:
https://doaj.org/article/9761791bdb6643649e47682863f473e4
Autor:
Christopher Leon, Alexander Scheinker
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract We utilize a Fourier transformation-based representation of Maxwell’s equations to develop physics-constrained neural networks for electrodynamics without gauge ambiguity, which we label the Fourier–Helmholtz–Maxwell neural operator me
Externí odkaz:
https://doaj.org/article/ca215c941a214b3eaa4c85f12a77a0ae
Autor:
Alexander Scheinker, Reeju Pokharel
Publikováno v:
APL Machine Learning, Vol 1, Iss 2, Pp 026109-026109-11 (2023)
We present a physics-constrained neural network (PCNN) approach to solving Maxwell’s equations for the electromagnetic fields of intense relativistic charged particle beams. We create a 3D convolutional PCNN to map time-varying current and charge d
Externí odkaz:
https://doaj.org/article/f21abe4daaf041578ae40bf41313eff0
Publikováno v:
Physical Review Accelerators and Beams, Vol 27, Iss 2, p 024601 (2024)
In this work, we develop a machine learning (ML) model with aleatoric uncertainty for the low energy beam transport (LEBT) region of the LANSCE linear accelerator in which we model the transport of a space-charge-dominated 750 keV proton beam through
Externí odkaz:
https://doaj.org/article/6dada45a51a547f696fe9ea528ba714c
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
Abstract 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
Externí odkaz:
https://doaj.org/article/1b5bd259315f456690de4a256e722122
Autor:
Alexander Scheinker, Simon Hirlaender, Francesco Maria Velotti, Spencer Gessner, Giovanni Zevi Della Porta, Verena Kain, Brennan Goddard, Rebecca Ramjiawan
Publikováno v:
AIP Advances, Vol 10, Iss 5, Pp 055320-055320-5 (2020)
Multi-objective optimization is important for particle accelerators where various competing objectives must be satisfied routinely such as, for example, transverse emittance vs bunch length. We develop and demonstrate an online multi-time scale multi
Externí odkaz:
https://doaj.org/article/dc3bc4b606fa4337996c3ef593e34e9f
Autor:
Alexander Scheinker
Publikováno v:
Information, Vol 12, Iss 4, p 161 (2021)
Machine learning (ML) is growing in popularity for various particle accelerator applications including anomaly detection such as faulty beam position monitor or RF fault identification, for non-invasive diagnostics, and for creating surrogate models.
Externí odkaz:
https://doaj.org/article/aa03565bb22a45f4b66aed1978099ca4
Publikováno v:
Information, Vol 12, Iss 2, p 61 (2021)
We discuss the implementation of a suite of virtual diagnostics at the FACET-II facility currently under commissioning at SLAC National Accelerator Laboratory. The diagnostics will be used for the prediction of the longitudinal phase space along the
Externí odkaz:
https://doaj.org/article/c479af8a958d4adc8a9e1d5b183fd9ca
Autor:
Alexander Scheinker, Dorian Bohler, Sergey Tomin, Raimund Kammering, Igor Zagorodnov, Holger Schlarb, Matthias Scholz, Bolko Beutner, Winfried Decking
Publikováno v:
Physical Review Accelerators and Beams, Vol 22, Iss 8, p 082802 (2019)
The output power of a free electron laser (FEL) has extremely high variance even when all FEL parameter set points are held constant because of the stochastic nature of the self-amplified spontaneous emission (SASE) FEL process, drift of thousands of
Externí odkaz:
https://doaj.org/article/fd621c76974f43c29f143c0aeaac025c