서브메뉴
검색
Anomalous Flavors and Triggers Precision Lepton Measurements and Real-Time Machine Learning at the CMS Detector
Anomalous Flavors and Triggers Precision Lepton Measurements and Real-Time Machine Learning at the CMS Detector
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202104822
- ISBN
- 9798291577011
- DDC
- 530
- 저자명
- Zipper, Noah.
- 서명/저자
- Anomalous Flavors and Triggers Precision Lepton Measurements and Real-Time Machine Learning at the CMS Detector
- 발행사항
- [Sl] : University of Colorado at Boulder, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 213 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-02, Section: B.
- 주기사항
- Advisor: Ulmer, Keith.
- 학위논문주기
- Thesis (Ph.D.)--University of Colorado at Boulder, 2025.
- 초록/해제
- 요약As the LHC physics program continues to collect data, with an eye towards upgrading to a High-Luminosity LHC later this decade, it is necessary for physicists to rethink the systems and data currently available, to both fully utilize existing resources and develop new methods for making impactful discoveries. This thesis presents work done as a part of the CMS collaboration, with a focus on testing lepton flavor universality (LFU) and developing real-time anomaly detection systems. After giving relevant theoretical and experimental background, this thesis will outline the precision measurement of the R(K) ratio, a critical test of LFU and the Standard Model. Using novel data-taking techniques like "B Parking" event storage and dynamic trigger thresholds, the CMS experiment is able to produce the most precise R(K) measurement done with a general-purpose detector to date. The following chapter will discuss AXOL1TL, an unsupervised anomaly detection algorithm. This algorithm uses an autoencoder to select interesting events without bias toward specific physics signatures. Implemented on FPGA hardware at the CMS Level-1 Trigger, AXOL1TL reads in detector inputs in real-time and makes decisions in nanoseconds. Such an approach is the first of its kind at the LHC, offering a new paradigm in the search for new physics. In summary, using existing datasets to perform precision tests of the Standard Model and implementing new data-taking strategies both seek to expand the physics reach of the LHC, now and in the future.
- 일반주제명
- Physics
- 일반주제명
- Energy
- 일반주제명
- Particle physics
- 일반주제명
- Computational physics
- 키워드
- Machine learning
- 기타저자
- University of Colorado at Boulder Physics
- 기본자료저록
- Dissertations Abstracts International. 87-02B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008260126s2025 us c eng d■001000017359022
■00520260202104822
■006m o d
■007cr#unu||||||||
■020 ▼a9798291577011
■035 ▼a(MiAaPQ)AAI32169509
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a530
■1001 ▼aZipper, Noah.▼0(orcid)0000-0002-4805-8020
■24510▼aAnomalous Flavors and Triggers Precision Lepton Measurements and Real-Time Machine Learning at the CMS Detector
■260 ▼a[Sl]▼bUniversity of Colorado at Boulder▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a213 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-02, Section: B.
■500 ▼aAdvisor: Ulmer, Keith.
■5021 ▼aThesis (Ph.D.)--University of Colorado at Boulder, 2025.
■520 ▼aAs the LHC physics program continues to collect data, with an eye towards upgrading to a High-Luminosity LHC later this decade, it is necessary for physicists to rethink the systems and data currently available, to both fully utilize existing resources and develop new methods for making impactful discoveries. This thesis presents work done as a part of the CMS collaboration, with a focus on testing lepton flavor universality (LFU) and developing real-time anomaly detection systems. After giving relevant theoretical and experimental background, this thesis will outline the precision measurement of the R(K) ratio, a critical test of LFU and the Standard Model. Using novel data-taking techniques like "B Parking" event storage and dynamic trigger thresholds, the CMS experiment is able to produce the most precise R(K) measurement done with a general-purpose detector to date. The following chapter will discuss AXOL1TL, an unsupervised anomaly detection algorithm. This algorithm uses an autoencoder to select interesting events without bias toward specific physics signatures. Implemented on FPGA hardware at the CMS Level-1 Trigger, AXOL1TL reads in detector inputs in real-time and makes decisions in nanoseconds. Such an approach is the first of its kind at the LHC, offering a new paradigm in the search for new physics. In summary, using existing datasets to perform precision tests of the Standard Model and implementing new data-taking strategies both seek to expand the physics reach of the LHC, now and in the future.
■590 ▼aSchool code: 0051.
■650 4▼aPhysics
■650 4▼aEnergy
■650 4▼aParticle physics
■650 4▼aComputational physics
■653 ▼aMachine learning
■653 ▼aLepton measurements
■653 ▼aLepton flavor universality
■653 ▼aAnomaly detection systems
■690 ▼a0605
■690 ▼a0798
■690 ▼a0216
■690 ▼a0791
■71020▼aUniversity of Colorado at Boulder▼bPhysics.
■7730 ▼tDissertations Abstracts International▼g87-02B.
■790 ▼a0051
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17359022▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
