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Dark Photons & Deep Learning: Unconventional Searches for New Physics at the Large Hadron Collider
Dark Photons & Deep Learning: Unconventional Searches for New Physics at the Large Hadron Collider
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152712
- ISBN
- 9798384053187
- DDC
- 593.7
- 서명/저자
- Dark Photons & Deep Learning: Unconventional Searches for New Physics at the Large Hadron Collider
- 발행사항
- [Sl] : Cornell University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 468 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
- 주기사항
- Advisor: Thom-Levy, Julia.
- 학위논문주기
- Thesis (Ph.D.)--Cornell University, 2024.
- 초록/해제
- 요약This thesis presents a pair of searches for new phenomena at the Large Hadron Collider using data collected by the Compact Muon Solenoid (CMS) experiment between 2016 and 2018. Both searches employ sophisticated new analysis techniques, some of which are being demonstrated on CMS data for the first time. The first search targets inelastic dark matter, a dark matter model predicting a unique collider signature involving missing transverse energy and soft, displaced leptons. The analysis makes use of novel reconstruction techniques for soft/displaced electrons and muons, and provides some of the first collider-based constraints on inelastic dark matter. The second analysis is a model-agnostic search for new dijet resonances at the TeV scale, and employs state-of-the-art machine learning-based anomaly detection techniques. The results demonstrate good sensitivity to a wide range of new physics scenarios, and constitute an important proof-of-principle for the techniques.
- 일반주제명
- Particle physics
- 일반주제명
- Physics
- 일반주제명
- Nuclear physics
- 키워드
- Dark matter
- 키워드
- Machine learning
- 키워드
- Pixel detectors
- 기타저자
- Cornell University Physics
- 기본자료저록
- Dissertations Abstracts International. 86-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798384053187
■035 ▼a(MiAaPQ)AAI31488769
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a593.7
■1001 ▼aBright-Thonney, Samuel.▼0(orcid)0000-0003-1889-7824
■24510▼aDark Photons & Deep Learning: Unconventional Searches for New Physics at the Large Hadron Collider
■260 ▼a[Sl]▼bCornell University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a468 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-03, Section: B.
■500 ▼aAdvisor: Thom-Levy, Julia.
■5021 ▼aThesis (Ph.D.)--Cornell University, 2024.
■520 ▼aThis thesis presents a pair of searches for new phenomena at the Large Hadron Collider using data collected by the Compact Muon Solenoid (CMS) experiment between 2016 and 2018. Both searches employ sophisticated new analysis techniques, some of which are being demonstrated on CMS data for the first time. The first search targets inelastic dark matter, a dark matter model predicting a unique collider signature involving missing transverse energy and soft, displaced leptons. The analysis makes use of novel reconstruction techniques for soft/displaced electrons and muons, and provides some of the first collider-based constraints on inelastic dark matter. The second analysis is a model-agnostic search for new dijet resonances at the TeV scale, and employs state-of-the-art machine learning-based anomaly detection techniques. The results demonstrate good sensitivity to a wide range of new physics scenarios, and constitute an important proof-of-principle for the techniques.
■590 ▼aSchool code: 0058.
■650 4▼aParticle physics
■650 4▼aPhysics
■650 4▼aNuclear physics
■653 ▼aAnomaly detection
■653 ▼aDark matter
■653 ▼aLong-lived particles
■653 ▼aMachine learning
■653 ▼aPixel detectors
■653 ▼aSemi-supervised learning
■690 ▼a0798
■690 ▼a0605
■690 ▼a0756
■690 ▼a0800
■71020▼aCornell University▼bPhysics.
■7730 ▼tDissertations Abstracts International▼g86-03B.
■790 ▼a0058
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17163473▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
