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Optimal Transport for High Energy Physics- [electronic resource]
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Optimal Transport for High Energy Physics- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101252
- ISBN
- 9798380154826
- DDC
- 530
- 저자명
- Cai, Tianji.
- 서명/저자
- Optimal Transport for High Energy Physics - [electronic resource]
- 발행사항
- [S.l.]: : University of California, Santa Barbara., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(202 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
- 주기사항
- Advisor: Craig, Nathaniel.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Santa Barbara, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약High energy physics, like many other scientific disciplines, has entered an exciting new era of big data, where both particle accelerators at the energy frontier and astrophysical surveys at the cosmic frontier are producing an enormous amount of data which may hold the very key to the most fundamental questions about nature. Mining such gold inevitably calls for revolutionary designs of ever more powerful and efficient statistical analysis frameworks, while at the same time scientific rigorousness places an additional requirement on the interpretability of any novel model proposed. Among a plethora of available modern machine learning techniques, the theory of optimal transport stands out as a distinct approach that is both high performing and mathematically well grounded. By equipping the space of data represented as distributions with a suitable metric, optimal transport replaces ad hoc notions of similarity with a well-defined distance, opening up a range of new applications with profound theoretical implications.This thesis introduces the theory of optimal transport with an eye towards its usage in physics. Special emphasis is put on two particular optimal transport distances which enjoy unique geometric properties. Utilizing their geometric structure, we develop a computationally efficient linearization framework for the two distances and highlight their approximations for discrete distributions encountered in practice. We then showcase the power of this linearized optimal transport framework by applying it to two use cases-one in collider physics at the energy frontier and the other in dark matter astrophysics at the cosmic frontier. As the adoption of optimal transport in high energy physics is still in its early stage, the present thesis invites the readers to think of other potential applications for their own research.
- 일반주제명
- Physics.
- 일반주제명
- Particle physics.
- 일반주제명
- Theoretical physics.
- 키워드
- Collider physics
- 키워드
- Energy frontier
- 키워드
- Cosmic frontier
- 기타저자
- University of California, Santa Barbara Physics
- 기본자료저록
- Dissertations Abstracts International. 85-03B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008240612s2023 us |||||||||||||||c||eng d■001000016933487
■00520240214101252
■006m o d
■007cr#unu||||||||
■020 ▼a9798380154826
■035 ▼a(MiAaPQ)AAI30529863
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a530
■1001 ▼aCai, Tianji.
■24510▼aOptimal Transport for High Energy Physics▼h[electronic resource]
■260 ▼a[S.l.]:▼bUniversity of California, Santa Barbara. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(202 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-03, Section: B.
■500 ▼aAdvisor: Craig, Nathaniel.
■5021 ▼aThesis (Ph.D.)--University of California, Santa Barbara, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aHigh energy physics, like many other scientific disciplines, has entered an exciting new era of big data, where both particle accelerators at the energy frontier and astrophysical surveys at the cosmic frontier are producing an enormous amount of data which may hold the very key to the most fundamental questions about nature. Mining such gold inevitably calls for revolutionary designs of ever more powerful and efficient statistical analysis frameworks, while at the same time scientific rigorousness places an additional requirement on the interpretability of any novel model proposed. Among a plethora of available modern machine learning techniques, the theory of optimal transport stands out as a distinct approach that is both high performing and mathematically well grounded. By equipping the space of data represented as distributions with a suitable metric, optimal transport replaces ad hoc notions of similarity with a well-defined distance, opening up a range of new applications with profound theoretical implications.This thesis introduces the theory of optimal transport with an eye towards its usage in physics. Special emphasis is put on two particular optimal transport distances which enjoy unique geometric properties. Utilizing their geometric structure, we develop a computationally efficient linearization framework for the two distances and highlight their approximations for discrete distributions encountered in practice. We then showcase the power of this linearized optimal transport framework by applying it to two use cases-one in collider physics at the energy frontier and the other in dark matter astrophysics at the cosmic frontier. As the adoption of optimal transport in high energy physics is still in its early stage, the present thesis invites the readers to think of other potential applications for their own research.
■590 ▼aSchool code: 0035.
■650 4▼aPhysics.
■650 4▼aParticle physics.
■650 4▼aTheoretical physics.
■650 4▼aHigh temperature physics.
■653 ▼aCollider physics
■653 ▼aOptimal transport
■653 ▼aEnergy frontier
■653 ▼aAstrophysical surveys
■653 ▼aCosmic frontier
■690 ▼a0605
■690 ▼a0798
■690 ▼a0753
■690 ▼a0597
■71020▼aUniversity of California, Santa Barbara▼bPhysics.
■7730 ▼tDissertations Abstracts International▼g85-03B.
■773 ▼tDissertation Abstract International
■790 ▼a0035
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16933487▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
