Bayesian optimization algorithms for accelerator physics
Accelerator physics relies on numerical algorithms to solve optimization problems in online
accelerator control and tasks such as experimental design and model calibration in …
accelerator control and tasks such as experimental design and model calibration in …
Four-dimensional phase-space reconstruction of flat and magnetized beams using neural networks and differentiable simulations
Beams with cross-plane coupling or extreme asymmetries between the two transverse
phase spaces are often encountered in particle accelerators. Flat beams with large …
phase spaces are often encountered in particle accelerators. Flat beams with large …
Efficient six-dimensional phase space reconstructions from experimental measurements using generative machine learning
R Roussel, JP Gonzalez-Aguilera, E Wisniewski… - … Review Accelerators and …, 2024 - APS
Next-generation accelerator concepts, which hinge on the precise shaping of beam
distributions, demand equally precise diagnostic methods capable of reconstructing beam …
distributions, demand equally precise diagnostic methods capable of reconstructing beam …
Progress in End-to-End Optimization of Detectors for Fundamental Physics with Differentiable Programming
M Aehle, L Arsini, RB Barreiro, A Belias, F Bury… - arXiv preprint arXiv …, 2023 - arxiv.org
In this article we examine recent developments in the research area concerning the creation
of end-to-end models for the complete optimization of measuring instruments. The models …
of end-to-end models for the complete optimization of measuring instruments. The models …
Efficient 6-dimensional phase space reconstruction from experimental measurements using generative machine learning
Next-generation accelerator concepts which hinge on the precise shaping of beam
distributions, demand equally precise diagnostic methods capable of reconstructing beam …
distributions, demand equally precise diagnostic methods capable of reconstructing beam …
Bridging the gap between machine learning and particle accelerator physics with high-speed, differentiable simulations
Machine learning has emerged as a powerful solution to the modern challenges in
accelerator physics. However, the limited availability of beam time, the computational cost of …
accelerator physics. However, the limited availability of beam time, the computational cost of …
JuTrack: a Julia package for auto-differentiable accelerator modeling and particle tracking
Efficient accelerator modeling and particle tracking are key for the design and configuration
of modern particle accelerators. In this work, we present JuTrack, a nested accelerator …
of modern particle accelerators. In this work, we present JuTrack, a nested accelerator …
arXiv: Progress in End-to-End Optimization of Detectors for Fundamental Physics with Differentiable Programming
In this article we examine recent developments in the research area concerning the creation
of end-to-end models for the complete optimization of measuring instruments. The models …
of end-to-end models for the complete optimization of measuring instruments. The models …
[PDF][PDF] Advancements in backwards differentiable beam dynamics simulations for accelerator design, model calibration, and machine learning
Many accelerator physics problems such as beamline design, beam dynamics model
calibration or interpreting experimental measurements rely on solving an optimization …
calibration or interpreting experimental measurements rely on solving an optimization …
[PDF][PDF] DETAILED CHARACTERIZATION OF COHERENT SYNCHROTRON RADIATION EFFECTS USING GENERATIVE PHASE SPACE RECONSTRUCTION
Coherent synchrotron radiation (CSR) in linear accelerators is detrimental to applications
that require highly compressed beams, such as FELs and wakefield accelerators. However …
that require highly compressed beams, such as FELs and wakefield accelerators. However …