Experimental Studies of Nonlinear Integrable Optics

State-of-the-art accelerators at energy and intensity frontiers require increasingly bright and powerful particle beams. In conventional linear lattices, intense beams suffer from collective instabilities, resulting in beam losses and maximum beam intensity limits. This thesis presents experimental studies of a novel lattice design concept, the nonlinear integrable optics (NIO), aimed at enhancing beam stability limits with little to no beam dynamics degradation. Single-particle beam dynamics measurements of two NIO devices, the quasi-integrable octupole system and the fully integrable Danilov-Nagaitsev system, were carried out at the purpose-built Integrable Optics Test Accelerator (IOTA) at Fermilab. Their simulation, hardware design, and commissioning process are presented. Extensive model and analysis algorithm development and benchmarking is described. Electron beam data from two scientific runs is analyzed, yielding frequency and phase space dynamics consistent with models. These results demonstrate viability and advantages of the NIO design, providing the groundwork for proton studies in the strong space-charge regime and future integrable accelerators

Nonlinear optics measurements in IOTA

Nonlinear integrable optics is a recently proposed accelerator lattice design approach which allows to generate an amplitude dependent tune shift which is needed in high brightness accelerators to mitigate fast coherent instabilities. Whereas usually octupoles are used to achieve this task, this concept allows doing so without exciting any resonances, in turn preventing any particle loss. The concept is based around a special magnet design, together with specific constraints on the optics of the accelerator. To study such a system, the Integrable Optics Test Accelerator (IOTA) was recently constructed and commissioned at Fermilab. For the assessment of the performance of this concept, good knowledge of the optics and the (non-)linear dynamics without the special magnet is of key importance. As such, measurements were conducted in the IOTA ring, using the captured turn-by-turn data by the beam position monitors after excitation to infer quantities such as amplitude detuning and resonance driving terms. In this note, first results of these measurements are presented

ChristopherMayes/Xopt: Xopt v2.0.0

<p>We are pleased the second major release of Xopt, with major changes of all high-level objects embrace the latest <a href="https://docs.pydantic.dev/latest/">Pydantic</a> V2. Some highlights are:</p> <ul> <li>Objects <code>Xopt</code>, <code>VOCS</code>, <code>Evaluator</code>, and all Generators inherit from <a href="https://docs.pydantic.dev/latest/api/base_model/#pydantic.BaseModel"><code>pydantic.BaseModel</code></a></li> <li>Xopt VOCS objects now contain "observables" attribute to store observed data that is not directly maximized or minimized for more complex algorithms (BAX)</li> <li>Asynchronous behavior is now implemented in a separate <code>AsynchronousXopt</code> object, which allows for the base <code>Xopt</code> object to be simpler.</li> <li>Multiple bugs with BO have been fixed, especially with constraints and trust region BO</li> <li>Enables new features including Heteroskedastic noise models, fixed feature GP models, and evaluate functions returning multiple observations for a single input dict</li> </ul> <p>Special thanks to @nikitakuklev for the careful migration from Pydantic v1 to v2.</p> <h2>Migration from V1</h2> <p>Users of previous versions will need to modify their YAML inputs. In particular, the <code>xopt:</code> field is no longer needed in the YAML, because its options are now at the top level. So,</p> <pre><code class="language-yaml">xopt: max_evalulations: 100 vocs: ... </code></pre> <p>Is now:</p> <pre><code class="language-yaml">max_evalulations: 100 vocs: ... </code></pre> <p>Pydantic will raise errors if fields are missing or if additional fields are not allowed.</p> <h2>What's Changed</h2> <ul> <li>model constructor bugfix by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/116</li> <li>Fix CustomMean by @t-bz in https://github.com/ChristopherMayes/Xopt/pull/117</li> <li>Add example for Bayesian Exploration with NaNs by @t-bz in https://github.com/ChristopherMayes/Xopt/pull/120</li> <li>Fix the ES and RCDS generators by @wenatuhs in https://github.com/ChristopherMayes/Xopt/pull/118</li> <li>update branch w main by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/121</li> <li>Fix the NelderMead generator and add basic tests by @wenatuhs in https://github.com/ChristopherMayes/Xopt/pull/122</li> <li>Pydantic generators by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/113</li> <li>Fix device inconsistencies by @t-bz in https://github.com/ChristopherMayes/Xopt/pull/123</li> <li>pull from main by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/124</li> <li>Bayes exp fix by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/125</li> <li>change default value of X.options.strict to True from False by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/126</li> <li>pip pydantic version for now by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/127</li> <li>enable evaluators to pass lists of return values for a single evaluate call by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/130</li> <li>pull in main by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/132</li> <li>fixed features for Bayesian generators by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/128</li> <li>bax algorithm saving by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/134</li> <li>Constrained turbo improvements by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/133</li> <li>ENH: Untility method to copy generator objects by @YektaY in https://github.com/ChristopherMayes/Xopt/pull/131</li> <li>Bugfixes by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/135</li> <li>Add ModelList functionality to BaxGenerator. by @dylanmkennedy in https://github.com/ChristopherMayes/Xopt/pull/137</li> <li>Computation time tracking by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/136</li> <li>Adaptive ucb ei by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/139</li> <li>implement and test heteroskedastic modeling by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/140</li> <li>improve error messaging for validating points in vocs by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/141</li> <li>Bugfixes and constrained optimization example by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/142</li> <li>Pydantic xopt object by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/143</li> <li>bugfix for using turbo with maximization + test by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/144</li> <li>implement and test data saving + restart bugfix by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/145</li> <li>bugfixes by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/147</li> <li>Migrate to pydantic v2 by @nikitakuklev in https://github.com/ChristopherMayes/Xopt/pull/129</li> <li>UCB and constraints by @t-bz in https://github.com/ChristopherMayes/Xopt/pull/149</li> <li>Update gh-pages.yml by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/152</li> <li>enable xopt init from yaml file by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/151</li> <li>bugfix implementation and test by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/153</li> <li>add from_file helper function by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/155</li> <li>Update README by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/146</li> <li>Improve model visualization options by @t-bz in https://github.com/ChristopherMayes/Xopt/pull/157</li> <li>bugfix w test by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/160</li> <li>Update main index page in docs by @ChristopherMayes in https://github.com/ChristopherMayes/Xopt/pull/161</li> <li>add select_best function to vocs by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/162</li> <li>Docs touchup by @ChristopherMayes in https://github.com/ChristopherMayes/Xopt/pull/164</li> <li>Ensure prior mean model is in eval mode by @t-bz in https://github.com/ChristopherMayes/Xopt/pull/163</li> <li>bugfix for pandas indexing issue by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/165</li> </ul> <h2>New Contributors</h2> <ul> <li>@YektaY made their first contribution in https://github.com/ChristopherMayes/Xopt/pull/131</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/ChristopherMayes/Xopt/compare/v1.4.1...v2.0.0</p&gt

ChristopherMayes/Xopt: Xopt v2.0.1

<h2>What's Changed</h2> <ul> <li>Delete print statement for validation of GP constructor by @t-bz in https://github.com/ChristopherMayes/Xopt/pull/169</li> <li>Optional arguments by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/170</li> <li>Vocs normalize inputs by @roussel-ryan in https://github.com/ChristopherMayes/Xopt/pull/168</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/ChristopherMayes/Xopt/compare/v2.0.0...v2.0.1</p&gt

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 simulations. The effectiveness of optimization algorithms in discovering ideal solutions for complex challenges with limited resources often determines the problem complexity these methods can address. The accelerator physics community has recognized the advantages of Bayesian optimization algorithms, which leverage statistical surrogate models of objective functions to effectively address complex optimization challenges, especially in the presence of noise during accelerator operation and in resource-intensive physics simulations. In this review article, we offer a conceptual overview of applying Bayesian optimization techniques towards solving optimization problems in accelerator physics. We begin by providing a straightforward explanation of the essential components that make up Bayesian optimization techniques. We then give an overview of current and previous work applying and modifying these techniques to solve accelerator physics challenges. Finally, we explore practical implementation strategies for Bayesian optimization algorithms to maximize their performance, enabling users to effectively address complex optimization challenges in real-time beam control and accelerator design

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 simulations. The effectiveness of optimization algorithms in discovering ideal solutions for complex challenges with limited resources often determines the problem complexity these methods can address. The accelerator physics community has recognized the advantages of Bayesian optimization algorithms, which leverage statistical surrogate models of objective functions to effectively address complex optimization challenges, especially in the presence of noise during accelerator operation and in resource-intensive physics simulations. In this review article, we offer a conceptual overview of applying Bayesian optimization techniques towards solving optimization problems in accelerator physics. We begin by providing a straightforward explanation of the essential components that make up Bayesian optimization techniques. We then give an overview of current and previous work applying and modifying these techniques to solve accelerator physics challenges. Finally, we explore practical implementation strategies for Bayesian optimization algorithms to maximize their performance, enabling users to effectively address complex optimization challenges in real-time beam control and accelerator design

First Results of the IOTA Ring Research at Fermilab

The IOTA ring at Fermilab is a unique machine exclusively dedicated to accelerator beam physics R&D. The research conducted at IOTA includes topics such as nonlinear integrable optics, suppression of coherent beam instabilities, optical stochastic cooling and quantum science experiments. In this talk we report on the first results of experiments with implementations of nonlinear integrable beam optics. The first of its kind practical realization of a two-dimensional integrable system in a strongly-focusing storage ring was demonstrated allowing among other things for stable beam circulation near or at the integer resonance. Also presented will be the highlights of the world’s first demonstration of optical stochastic beam cooling and other selected results of IOTA’s broad experimental program