Input Beam Matching and Beam Dynamics Design Optimization of the IsoDAR RFQ using Statistical and Machine Learning Techniques

Koser, Daniel; Waites, Loyd; Winklehner, Daniel; Frey, Matthias; Adelmann, Andreas; Conrad, J. M.

doi:10.48550/arxiv.2112.02579

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…in Ref. [60,206]). This is important for understanding beam dynamics effects in the observed system.…”

Section: Differentiable Physics Simulationsmentioning

confidence: 94%

Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper

Dorigo¹,

Giammanco²,

Vischia³

et al. 2022

Preprint

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The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a superhuman task, given the large dimensionality of the space of possible choices for geometry, detection technology, materials, data-acquisition, and informationextraction techniques, and the interdependence of the related parameters. On the other hand, massive potential gains in performance over standard, "experience-driven" layouts are in principle within our reach if an objective function fully aligned with the final goals of the instrument is maximized by means of a systematic search of the configuration space. The stochastic nature of the involved quantum processes make the modeling of these systems an intractable problem from a classical statistics point of view, yet the construction of a fully differentiable pipeline and the use of deep learning techniques may allow the simultaneous optimization of all design parameters.In this document we lay down our plans for the design of a modular and versatile modeling tool for the end-to-end optimization of complex instruments for particle physics experiments as well as industrial and medical applications that share the detection of radiation as their basic ingredient. We consider a selected set of use cases to highlight the specific needs of different applications.

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“…in Ref. [60,206]). This is important for understanding beam dynamics effects in the observed system.…”

Section: Differentiable Physics Simulationsmentioning

confidence: 94%

Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper

Dorigo¹,

Giammanco²,

Vischia³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Since the 2013 Snowmass Study, cyclotron development has culminated in the design described in Ref. [505], which presents start-to-end simulations and prototypes of components now under test [506,507]. As seen in Fig.…”

Section: Isodarmentioning

confidence: 99%

White Paper on Light Sterile Neutrino Searches and Related Phenomenology

Acero¹,

Argüelles²,

Hostert³

et al. 2022

Preprint

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“…There is significant promise of applications of AI/ML in beam diagnostics, controls, and modeling [69]. Opportunity exists in broadening AI/ML methods for early detection of a broad range of accelerator component or subsystem failures [70] and for optimization of advanced numerical simulations through identification of the most promising combinations of parameters thereby reducing the total number of required simulations ( [71]).…”

Section: Artificial Intelligence and Machine Learning For Acceleratorsmentioning

confidence: 99%

Challenges of Future Accelerators for Particle Physics Research

2022

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For over half a century, high-energy particle accelerators have been a major enabling technology for particle and nuclear physics research as well as sources of X-rays for photon science research in material science, chemistry and biology. Particle accelerators for energy and intensity Frontier research in particle and nuclear physics continuously push the accelerator community to invent ways to increase the energy and improve the performance of accelerators, reduce their cost, and make them more power efficient. The accelerator community has demonstrated imagination and creativity in developing a plethora of future accelerator ideas and proposals. The technical maturity of the proposed facilities ranges from shovel-ready to those that are still largely conceptual. At this time, over 100 contributed papers have been submitted to the Accelerator Frontier of the US particle physics decadal community planning exercise known as Snowmass’2021. These papers cover a broad spectrum of topics: beam physics and accelerator education, accelerators for neutrinos, colliders for Electroweak/Higgs studies and multi-TeV energies, accelerators for Physics Beyond Colliders and rare processes, advanced accelerator concepts, and accelerator technology for Radio Frequency cavities (RF), magnets, targets and sources. This paper provides an overview of the present state of accelerators for particle physics and gives a brief description of some of the major facilities that have been proposed, their perceived advantages and some of the remaining challenges.

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Input Beam Matching and Beam Dynamics Design Optimization of the IsoDAR RFQ using Statistical and Machine Learning Techniques

Cited by 4 publications

References 18 publications

Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper

Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper

White Paper on Light Sterile Neutrino Searches and Related Phenomenology

Challenges of Future Accelerators for Particle Physics Research

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