24 research outputs found
Phase Space Reconstruction from Accelerator Beam Measurements Using Neural Networks and Differentiable Simulations
Characterizing the phase space distribution of particle beams in accelerators
is a central part of accelerator understanding and performance optimization.
However, conventional reconstruction-based techniques either use simplifying
assumptions or require specialized diagnostics to infer high-dimensional (
2D) beam properties. In this Letter, we introduce a general-purpose algorithm
that combines neural networks with differentiable particle tracking to
efficiently reconstruct high-dimensional phase space distributions without
using specialized beam diagnostics or beam manipulations. We demonstrate that
our algorithm accurately reconstructs detailed 4D phase space distributions
with corresponding confidence intervals in both simulation and experiment using
a single focusing quadrupole and diagnostic screen. This technique allows for
the measurement of multiple correlated phase spaces simultaneously, which will
enable simplified 6D phase space distribution reconstructions in the future
Multipoint-BAX: A New Approach for Efficiently Tuning Particle Accelerator Emittance via Virtual Objectives
Although beam emittance is critical for the performance of high-brightness
accelerators, optimization is often time limited as emittance calculations,
commonly done via quadrupole scans, are typically slow. Such calculations are a
type of , i.e. each query requires multiple
secondary measurements. Traditional black-box optimizers such as Bayesian
optimization are slow and inefficient when dealing with such objectives as they
must acquire the full series of measurements, but return only the emittance,
with each query. We propose applying Bayesian Algorithm Execution (BAX) to
instead query and model individual beam-size measurements. BAX avoids the slow
multi-point query on the accelerator by acquiring points through a
, i.e. calculating the emittance objective from a
fast learned model rather than directly from the accelerator. Here, we use BAX
to minimize emittance at the Linac Coherent Light Source (LCLS) and the
Facility for Advanced Accelerator Experimental Tests II (FACET-II). In
simulation, BAX is 20 faster and more robust to noise compared to
existing methods. In live LCLS and FACET-II tests, BAX performed the first
automated emittance tuning, matching the hand-tuned emittance at FACET-II and
achieving a 24% lower emittance at LCLS. Our method represents a conceptual
shift for optimizing multi-point queries, and we anticipate that it can be
readily adapted to similar problems in particle accelerators and other
scientific instruments
Compact ring-based X-ray source with on-orbit and on-energy laser-plasma injection
We report here the results of a one week long investigation into the
conceptual design of an X-ray source based on a compact ring with on-orbit and
on-energy laser-plasma accelerator. We performed these studies during the June
2016 USPAS class "Physics of Accelerators, Lasers, and Plasma..." applying the
art of inventiveness TRIZ. We describe three versions of the light source with
the constraints of the electron beam with energy or
and a magnetic lattice design being normal conducting (only for the
beam) or superconducting (for either beam). The electron beam
recirculates in the ring, to increase the effective photon flux. We describe
the design choices, present relevant parameters, and describe insights into
such machines.Comment: 4 pages, 1 figure, Conference Proceedings of NAPAC 201
Neural Networks for Modeling and Control of Particle Accelerators
Charged particle accelerators support a wide variety of scientific, industrial, and medical applications. They range in scale and complexity from systems with just a few components for beam acceleration and manipulation, to large scientific user facilities that span many kilometers and have hundreds-to-thousands of individually-controllable components. Specific operational requirements must be met by adjusting the many controllable variables of the accelerator. Meeting these requirements can be challenging, both in terms of the ability to achieve specific beam quality metrics in a reliable fashion and in terms of the time needed to set up and maintain the optimal operating conditions. One avenue toward addressing this challenge is to incorporate techniques from the fields of machine learning (ML) and artificial intelligence (AI) into the way particle accelerators are modeled and controlled. While many promising approaches within AI/ML could be used for particle accelerators, this dissertation focuses on approaches based on neural networks. Neural networks are particularly well-suited to modeling, control, and diagnostic analysis of nonlinear systems, as well as systems with large parameter spaces. They are also very appealing for their ability to process high-dimensional data types, such as images and time series (both of which are ubiquitous in particle accelerators). In this work, key studies that demonstrated the potential utility of modern neural network-based approaches to modeling and control of particle accelerators are presented. The context for this work is important: at the start of this work in 2012, there was little interest in AI/ML in the particle accelerator community, and many of the advances in neural networks and deep learning that enabled its present success had not yet been made at that time. As such, this work was both an exploration of possible application areas and a generator of proof-of-concept demonstrations in these areas
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Multiobjective Bayesian optimization for online accelerator tuning
Particle accelerators require constant tuning during operation to meet beam quality, total charge and particle energy requirements for use in a wide variety of physics, chemistry and biology experiments. Maximizing the performance of an accelerator facility often necessitates multi-objective optimization, where operators must balance trade-offs between multiple objectives simultaneously, often using limited, temporally expensive beam observations. Usually, accelerator optimization problems are solved offline, prior to actual operation, with advanced beamline simulations and parallelized optimization methods (NSGA-II, Swarm Optimization). Unfortunately, it is not feasible to use these methods for online multi-objective optimization, since beam measurements can only be done in a serial fashion, and these optimization methods require a large number of measurements to converge to a useful solution.Here, we introduce a multi-objective Bayesian optimization scheme, which finds the full Pareto front of an accelerator optimization problem efficiently in a serialized manner and is thus a critical step towards practical online multi-objective optimization in accelerators.This method uses a set of Gaussian process surrogate models, along with a multi-objective acquisition function, which reduces the number of observations needed to converge by at least an order of magnitude over current methods.We demonstrate how this method can be modified to specifically solve optimization challenges posed by the tuning of accelerators.This includes the addition of optimization constraints, objective preferences and costs related to changing accelerator parameters
Virtual Diagnostic Suite for Electron Beam Prediction and Control at FACET-II
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 linac, spectral reconstruction of the bunch profile, and non-destructive inference of transverse beam quality (emittance) while using edge radiation at the injector dogleg and bunch compressor locations. These measurements will be folded into adaptive feedbacks and Machine Learning (ML)-based reinforcement learning controls to improve the stability and optimize the performance of the machine for different experimental configurations. In this paper we describe each of these diagnostics with expected measurement results that are based on simulation data and discuss progress towards implementation in regular operations