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Buried in Background: Hunting for Physics Beyond the Standard Model- [electronic resource]
Buried in Background: Hunting for Physics Beyond the Standard Model- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101252
- ISBN
- 9798380154833
- DDC
- 593.7
- 서명/저자
- Buried in Background: Hunting for Physics Beyond the Standard Model - [electronic resource]
- 발행사항
- [S.l.]: : University of California, Santa Barbara., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(179 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
- 요약As experimental efforts to uncover the nature of physics beyond the Standard Model continue to push the boundaries of energy and sensitivity, theoretical predictions of well-motivated physics models will need to commensurately increase in precision so that we may reliably distinguish signal from background. A prime example may be found in the stochastic gravitational wave background that will soon be within reach of observatories and which could contain imprints from a variety of ultraviolet phenomena, including first order cosmological phase transitions and topological defects. We begin this thesis by discussing scenarios in which the gravitational wave spectrum due to a phase transition can be substantially altered by particle reflection off of relativistic bubble walls, an effect which has been largely ignored in the literature thus far. We then move on to discussing a particular class of parity-based solutions to the strong CP problem which also features a potential gravitational wave signal, this time due to domain wall topological defects. In addition, these models provide testable predictions for near-future colliders and tabletop experiments. Finally, we point out an exciting new computational avenue for discriminating between signal and background in particle collider data using machine learning coupled with a physically motivated metric on the space of collider events.
- 일반주제명
- Particle physics.
- 일반주제명
- Physics.
- 키워드
- Colliders
- 키워드
- Friction
- 키워드
- Gravity
- 키워드
- Machine learning
- 키워드
- Standard model
- 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
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■00520240214101252
■006m o d
■007cr#unu||||||||
■020 ▼a9798380154833
■035 ▼a(MiAaPQ)AAI30529869
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a593.7
■1001 ▼aKoszegi, Giacomo.
■24510▼aBuried in Background: Hunting for Physics Beyond the Standard Model▼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(179 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 ▼aAs experimental efforts to uncover the nature of physics beyond the Standard Model continue to push the boundaries of energy and sensitivity, theoretical predictions of well-motivated physics models will need to commensurately increase in precision so that we may reliably distinguish signal from background. A prime example may be found in the stochastic gravitational wave background that will soon be within reach of observatories and which could contain imprints from a variety of ultraviolet phenomena, including first order cosmological phase transitions and topological defects. We begin this thesis by discussing scenarios in which the gravitational wave spectrum due to a phase transition can be substantially altered by particle reflection off of relativistic bubble walls, an effect which has been largely ignored in the literature thus far. We then move on to discussing a particular class of parity-based solutions to the strong CP problem which also features a potential gravitational wave signal, this time due to domain wall topological defects. In addition, these models provide testable predictions for near-future colliders and tabletop experiments. Finally, we point out an exciting new computational avenue for discriminating between signal and background in particle collider data using machine learning coupled with a physically motivated metric on the space of collider events.
■590 ▼aSchool code: 0035.
■650 4▼aParticle physics.
■650 4▼aPhysics.
■653 ▼aColliders
■653 ▼aFriction
■653 ▼aGravity
■653 ▼aMachine learning
■653 ▼aStandard model
■690 ▼a0798
■690 ▼a0605
■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=T16933490▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
