Construction of approximate invariants for nonintegrable Hamiltonian systems

We present a method to construct high-order polynomial approximate invariants (AI) for nonintegrable Hamiltonian dynamical systems and apply it to a modern ring-based particle accelerator. Taking advantage of a special property of one-turn transformation maps expressed as square matrices, AIs can be...

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Main Authors: Yongjun Li, Derong Xu, Yue Hao
Format: Article
Language:English
Published: American Physical Society 2025-07-01
Series:Physical Review Accelerators and Beams
Online Access:http://doi.org/10.1103/m349-wmnr
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author Yongjun Li
Derong Xu
Yue Hao
author_facet Yongjun Li
Derong Xu
Yue Hao
author_sort Yongjun Li
collection DOAJ
description We present a method to construct high-order polynomial approximate invariants (AI) for nonintegrable Hamiltonian dynamical systems and apply it to a modern ring-based particle accelerator. Taking advantage of a special property of one-turn transformation maps expressed as square matrices, AIs can be constructed order by order iteratively. Evaluating AI with simulation data, we observe that AI’s fluctuation is actually a measure of chaos. Through minimizing the fluctuations, the stable region of long-term motions, i.e., the dynamic aperture of the accelerator, could be enlarged.
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issn 2469-9888
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publishDate 2025-07-01
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series Physical Review Accelerators and Beams
spelling doaj-art-59b0b46f5bf640e0bb322e28ec5ab6bb2025-08-20T03:18:02ZengAmerican Physical SocietyPhysical Review Accelerators and Beams2469-98882025-07-0128707400110.1103/m349-wmnrConstruction of approximate invariants for nonintegrable Hamiltonian systemsYongjun LiDerong XuYue HaoWe present a method to construct high-order polynomial approximate invariants (AI) for nonintegrable Hamiltonian dynamical systems and apply it to a modern ring-based particle accelerator. Taking advantage of a special property of one-turn transformation maps expressed as square matrices, AIs can be constructed order by order iteratively. Evaluating AI with simulation data, we observe that AI’s fluctuation is actually a measure of chaos. Through minimizing the fluctuations, the stable region of long-term motions, i.e., the dynamic aperture of the accelerator, could be enlarged.http://doi.org/10.1103/m349-wmnr
spellingShingle Yongjun Li
Derong Xu
Yue Hao
Construction of approximate invariants for nonintegrable Hamiltonian systems
Physical Review Accelerators and Beams
title Construction of approximate invariants for nonintegrable Hamiltonian systems
title_full Construction of approximate invariants for nonintegrable Hamiltonian systems
title_fullStr Construction of approximate invariants for nonintegrable Hamiltonian systems
title_full_unstemmed Construction of approximate invariants for nonintegrable Hamiltonian systems
title_short Construction of approximate invariants for nonintegrable Hamiltonian systems
title_sort construction of approximate invariants for nonintegrable hamiltonian systems
url http://doi.org/10.1103/m349-wmnr
work_keys_str_mv AT yongjunli constructionofapproximateinvariantsfornonintegrablehamiltoniansystems
AT derongxu constructionofapproximateinvariantsfornonintegrablehamiltoniansystems
AT yuehao constructionofapproximateinvariantsfornonintegrablehamiltoniansystems