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 multipoint query , i.e. each query requires multiple secondar...
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| Published in: | Machine learning: science and technology Vol. 5; no. 1; pp. 15004 - 15019 |
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| Main Authors: | , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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01.03.2024
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| ISSN: | 2632-2153, 2632-2153 |
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| Abstract | 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
multipoint query
, 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 a new information-theoretic algorithm,
Multipoint-BAX
, for black-box optimization on multipoint queries, which queries and models individual beam-size measurements using techniques from Bayesian Algorithm Execution (BAX). Our method avoids the slow multipoint query on the accelerator by acquiring points through a
virtual objective
, i.e. calculating the emittance objective from a fast learned model rather than directly from the accelerator. We use
Multipoint-BAX
to minimize emittance at the Linac Coherent Light Source (LCLS) and the Facility for Advanced Accelerator Experimental Tests II (FACET-II). In simulation, our method is 20× faster and more robust to noise compared to existing methods. In live tests, it matched the hand-tuned emittance at FACET-II and achieved a 24% lower emittance than hand-tuning at LCLS. Our method represents a conceptual shift for optimizing multipoint queries, and we anticipate that it can be readily adapted to similar problems in particle accelerators and other scientific instruments. |
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| AbstractList | 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 multipoint query, 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 a new information-theoretic algorithm, Multipoint-BAX, for black-box optimization on multipoint queries, which queries and models individual beam-size measurements using techniques from Bayesian Algorithm Execution (BAX). Our method avoids the slow multipoint query on the accelerator by acquiring points through a virtual objective, i.e. calculating the emittance objective from a fast learned model rather than directly from the accelerator. We use Multipoint-BAX to minimize emittance at the Linac Coherent Light Source (LCLS) and the Facility for Advanced Accelerator Experimental Tests II (FACET-II). In simulation, our method is 20× faster and more robust to noise compared to existing methods. In live tests, it matched the hand-tuned emittance at FACET-II and achieved a 24% lower emittance than hand-tuning at LCLS. Our method represents a conceptual shift for optimizing multipoint queries, and we anticipate that it can be readily adapted to similar problems in particle accelerators and other scientific instruments. 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 multipoint query , 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 a new information-theoretic algorithm, Multipoint-BAX , for black-box optimization on multipoint queries, which queries and models individual beam-size measurements using techniques from Bayesian Algorithm Execution (BAX). Our method avoids the slow multipoint query on the accelerator by acquiring points through a virtual objective , i.e. calculating the emittance objective from a fast learned model rather than directly from the accelerator. We use Multipoint-BAX to minimize emittance at the Linac Coherent Light Source (LCLS) and the Facility for Advanced Accelerator Experimental Tests II (FACET-II). In simulation, our method is 20× faster and more robust to noise compared to existing methods. In live tests, it matched the hand-tuned emittance at FACET-II and achieved a 24% lower emittance than hand-tuning at LCLS. Our method represents a conceptual shift for optimizing multipoint queries, and we anticipate that it can be readily adapted to similar problems in particle accelerators and other scientific instruments. |
| Author | Mayes, Christopher Neiswanger, Willie Emma, Claudio Ermon, Stefano Colocho, William Garrahan, Jacqueline Maxwell, Timothy Edelen, Auralee Ayoub Miskovich, Sara Ratner, Daniel |
| Author_xml | – sequence: 1 givenname: Sara orcidid: 0000-0002-3302-838X surname: Ayoub Miskovich fullname: Ayoub Miskovich, Sara organization: SLAC National Accelerator Laboratory , Menlo Park, CA, United States of America – sequence: 2 givenname: Willie orcidid: 0000-0002-9619-5572 surname: Neiswanger fullname: Neiswanger, Willie organization: Department of Computer Science, Stanford University , Stanford, CA, United States of America – sequence: 3 givenname: William surname: Colocho fullname: Colocho, William organization: SLAC National Accelerator Laboratory , Menlo Park, CA, United States of America – sequence: 4 givenname: Claudio surname: Emma fullname: Emma, Claudio organization: SLAC National Accelerator Laboratory , Menlo Park, CA, United States of America – sequence: 5 givenname: Jacqueline surname: Garrahan fullname: Garrahan, Jacqueline organization: SLAC National Accelerator Laboratory , Menlo Park, CA, United States of America – sequence: 6 givenname: Timothy surname: Maxwell fullname: Maxwell, Timothy organization: SLAC National Accelerator Laboratory , Menlo Park, CA, United States of America – sequence: 7 givenname: Christopher surname: Mayes fullname: Mayes, Christopher organization: SLAC National Accelerator Laboratory , Menlo Park, CA, United States of America – sequence: 8 givenname: Stefano surname: Ermon fullname: Ermon, Stefano organization: Department of Computer Science, Stanford University , Stanford, CA, United States of America – sequence: 9 givenname: Auralee surname: Edelen fullname: Edelen, Auralee organization: SLAC National Accelerator Laboratory , Menlo Park, CA, United States of America – sequence: 10 givenname: Daniel surname: Ratner fullname: Ratner, Daniel organization: SLAC National Accelerator Laboratory , Menlo Park, CA, United States of America |
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| Cites_doi | 10.1103/PhysRevSTAB.10.034801 10.2514/1.J052940 10.1038/s41467-021-25757-3 10.1093/bioinformatics/btx638 10.1103/PhysRevSTAB.18.084001 10.1103/PhysRevSTAB.4.053501 10.1103/PhysRevMaterials.2.013803 10.1103/PhysRevAccelBeams.23.114201 10.1103/PhysRevLett.124.124801 10.1038/s41467-020-20245-6 10.1109/TCST.2017.2664728 10.1093/comjnl/7.4.308 10.1103/PhysRevSTAB.9.044204 10.1023/A:1008306431147 10.1016/j.nima.2016.10.041 10.1103/PhysRevAccelBeams.22.082802 10.1103/PhysRevAccelBeams.24.062801 10.1109/PLASMA.1991.695803 10.1103/PhysRevAccelBeams.21.104601 10.1038/s41592-019-0686-2 10.1103/PhysRevAccelBeams.22.101301 10.1103/PhysRevSTAB.18.101002 10.2172/395453 10.1016/j.md.2016.04.001 10.2172/2283895 10.1103/PhysRevSTAB.9.031001 10.1103/PhysRevSTAB.11.030703 10.1103/PhysRevAccelBeams.22.054601 10.1109/PAC.1995.504603 10.1103/PhysRevSTAB.16.102803 10.1038/nphoton.2010.176 10.1021/acs.accounts.0c00713 10.1103/PhysRevAccelBeams.25.044601 10.1103/PhysRevAccelBeams.24.072802 |
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| SubjectTerms | Algorithms Bayesian algorithm execution Bayesian analysis Bayesian optimization Black boxes Coherent light Emittance Information theory Light sources Linear accelerators multipoint optimization online optimization Optimization particle accelerator PARTICLE ACCELERATORS Quadrupoles Queries Tuning x-ray free electron laser |
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| Title | Multipoint-BAX: a new approach for efficiently tuning particle accelerator emittance via virtual objectives |
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