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 …
Reinforcement learning-trained optimisers and Bayesian optimisation for online particle accelerator tuning
Online tuning of particle accelerators is a complex optimisation problem that continues to
require manual intervention by experienced human operators. Autonomous tuning is a …
require manual intervention by experienced human operators. Autonomous tuning is a …
Large language models for human-machine collaborative particle accelerator tuning through natural language
Autonomous tuning of particle accelerators is an active and challenging research field with
the goal of enabling advanced accelerator technologies and cutting-edge high-impact …
the goal of enabling advanced accelerator technologies and cutting-edge high-impact …
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 …
Multi-objective Bayesian active learning for MeV-ultrafast electron diffraction
Ultrafast electron diffraction using MeV energy beams (MeV-UED) has enabled
unprecedented scientific opportunities in the study of ultrafast structural dynamics in a …
unprecedented scientific opportunities in the study of ultrafast structural dynamics in a …
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 …
Demonstration of autonomous emittance characterization at the argonne wakefield accelerator
Transverse beam emittance plays a key role in the performance of high-brightness
accelerators. Characterizing beam emittance is often carried out using a quadrupole scan …
accelerators. Characterizing beam emittance is often carried out using a quadrupole scan …
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 …
[PDF][PDF] How can machine learning help future light sources?
Abstract Machine learning (ML) is one of the key technologies that can considerably extend
and advance the capabilities of particle accelerators and needs to be included in their future …
and advance the capabilities of particle accelerators and needs to be included in their future …
[PDF][PDF] Multi-objective genetic optimization of high charge TopGun photoinjector
PM Anisimov, EI Simakov, H Xu, M Kaemingk… - Proc. IPAC'24 - jacow.org
The TopGun photoinjector is a 1.6-cell C-band gun developed by the University of
California, Los Angeles team. Originally optimized for 100 pC operation, its low emittance …
California, Los Angeles team. Originally optimized for 100 pC operation, its low emittance …