서브메뉴
검색
Electron Spin Polarization Preservation in the Electron-Ion Collider
Electron Spin Polarization Preservation in the Electron-Ion Collider
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105313
- ISBN
- 9798273309616
- DDC
- 530
- 서명/저자
- Electron Spin Polarization Preservation in the Electron-Ion Collider
- 발행사항
- [Sl] : Cornell University, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 209 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-07, Section: B.
- 주기사항
- Advisor: Hoffstaetter de Torquat, Georg.
- 학위논문주기
- Thesis (Ph.D.)--Cornell University, 2025.
- 초록/해제
- 요약We present various works aimed at maximizing polarization in the Electron Storage Ring (ESR) of the soon-to-be-built Electron-Ion Collider (EIC), and describe in detail the polarization properties of the ESR throughout its design evolution. Most significantly, we present a novel method called "Best Adjustment Groups for ELectron Spin" (BAGELS) that achieves simultaneous control of the polarization, orbit, and optics in electron storage rings by use of special vertical orbit bumps constructed via dimensionality reduction. Using BAGELS, we nearly double the asymptotic polarization in a 1-interaction point (IP) ESR lattice, and more than triple it in a 2-IP lattice. We also use BAGELS to construct knobs that can be used for global coupling correction, and knobs that generate vertical emittance for beam size matching, all while having minimal impacts on the polarization and orbit/optics. Furthermore, we present SciBmad, a new, modular, differentiable, and high performance accelerator physics software that can be used easily in Python or Julia. SciBmad's symplectic integrators, which include spin, are universally polymorphic, forwards-/backwards-/Taylor-differentiable, and CPU/GPU parallelizable. Also included are a high-order automatic differentiation library, and routines for doing perturbation theory with nonlinear (possibly damped) Hamiltonian maps using Lie algebraic methods. SciBmad's machine learning-enabled ecosystem aims to be a powerful tool for modern particle accelerator design and simulation.
- 일반주제명
- Physics
- 일반주제명
- Particle physics
- 일반주제명
- Computational physics
- 키워드
- Beam dynamics
- 키워드
- Storage rings
- 기타저자
- Cornell University Physics
- 기본자료저록
- Dissertations Abstracts International. 87-07B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008260126s2025 us c eng d■001000017360161
■00520260202105313
■006m o d
■007cr#unu||||||||
■020 ▼a9798273309616
■035 ▼a(MiAaPQ)AAI32285200
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a530
■1001 ▼aSignorelli, Matthew George.
■24510▼aElectron Spin Polarization Preservation in the Electron-Ion Collider
■260 ▼a[Sl]▼bCornell University▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a209 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-07, Section: B.
■500 ▼aAdvisor: Hoffstaetter de Torquat, Georg.
■5021 ▼aThesis (Ph.D.)--Cornell University, 2025.
■520 ▼aWe present various works aimed at maximizing polarization in the Electron Storage Ring (ESR) of the soon-to-be-built Electron-Ion Collider (EIC), and describe in detail the polarization properties of the ESR throughout its design evolution. Most significantly, we present a novel method called "Best Adjustment Groups for ELectron Spin" (BAGELS) that achieves simultaneous control of the polarization, orbit, and optics in electron storage rings by use of special vertical orbit bumps constructed via dimensionality reduction. Using BAGELS, we nearly double the asymptotic polarization in a 1-interaction point (IP) ESR lattice, and more than triple it in a 2-IP lattice. We also use BAGELS to construct knobs that can be used for global coupling correction, and knobs that generate vertical emittance for beam size matching, all while having minimal impacts on the polarization and orbit/optics. Furthermore, we present SciBmad, a new, modular, differentiable, and high performance accelerator physics software that can be used easily in Python or Julia. SciBmad's symplectic integrators, which include spin, are universally polymorphic, forwards-/backwards-/Taylor-differentiable, and CPU/GPU parallelizable. Also included are a high-order automatic differentiation library, and routines for doing perturbation theory with nonlinear (possibly damped) Hamiltonian maps using Lie algebraic methods. SciBmad's machine learning-enabled ecosystem aims to be a powerful tool for modern particle accelerator design and simulation.
■590 ▼aSchool code: 0058.
■650 4▼aPhysics
■650 4▼aParticle physics
■650 4▼aComputational physics
■653 ▼aAccelerator physics
■653 ▼aBeam dynamics
■653 ▼aNonlinear dynamics
■653 ▼aSpin polarization
■653 ▼aStorage rings
■690 ▼a0605
■690 ▼a0798
■690 ▼a0216
■71020▼aCornell University▼bPhysics.
■7730 ▼tDissertations Abstracts International▼g87-07B.
■790 ▼a0058
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17360161▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
