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Emerging Jets Search, Triton Server Deployment, and Track Quality Development Machine Learning Applications in High Energy Physics
Emerging Jets Search, Triton Server Deployment, and Track Quality Development Machine Learning Applications in High Energy Physics
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151126
- ISBN
- 9798382717371
- DDC
- 593.7
- 저자명
- Savard, Claire.
- 서명/저자
- Emerging Jets Search, Triton Server Deployment, and Track Quality Development Machine Learning Applications in High Energy Physics
- 발행사항
- [Sl] : University of Colorado at Boulder, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 175 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
- 주기사항
- Advisor: Stenson, Kevin.
- 학위논문주기
- Thesis (Ph.D.)--University of Colorado at Boulder, 2024.
- 초록/해제
- 요약Machine learning is becoming prevalent in high energy physics, with numerous applications in physics analyses and event reconstruction showing great improvements compared to traditional computing methods. This thesis studies three projects which each propose new avenues for machine learning applications within the high energy physics CMS experiment located at CERN. In the first project, a search for a dark matter signal called "emerging jets" is performed, using graph neural networks to greatly increase sensitivity to the signal's signature within the data. The result of this dark matter search sets the most stringent exclusion limits to date on theoretical emerging jet models. Motivated by inefficiencies encountered when processing the emerging jet graph neural network at Fermi National Accelerator Laboratory's computing centers, the second project re-optimizes the computing centers for machine learning inference. This re-optimization uses NVIDIA Triton Inference Servers to process users' analysis code heterogeneously, therefore achieving high processing throughput and decreasing user time-to-insight. The last project focuses on an upgrade to the CMS experiment's real-time event selection system which improves physics object reconstruction under harsh processing conditions. A boosted decision tree is used to quickly and efficiently quantify a reconstructed particle's "track quality" in order to remove particle tracks reconstructed erroneously. In summary, this thesis will not only present examples of how high energy physics can greatly benefit by leveraging machine learning techniques for physics analysis and reconstruction, but will also provide guidance on how the field can prepare for the inevitable increase in machine learning applications.
- 일반주제명
- Particle physics
- 일반주제명
- Computer science
- 일반주제명
- Computational physics
- 키워드
- Dark matter
- 키워드
- Machine learning
- 키워드
- Emerging jets
- 기타저자
- University of Colorado at Boulder Physics
- 기본자료저록
- Dissertations Abstracts International. 85-11B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798382717371
■035 ▼a(MiAaPQ)AAI31147012
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a593.7
■1001 ▼aSavard, Claire.▼0(orcid)0009-0000-7507-0570
■24510▼aEmerging Jets Search, Triton Server Deployment, and Track Quality Development Machine Learning Applications in High Energy Physics
■260 ▼a[Sl]▼bUniversity of Colorado at Boulder▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a175 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-11, Section: B.
■500 ▼aAdvisor: Stenson, Kevin.
■5021 ▼aThesis (Ph.D.)--University of Colorado at Boulder, 2024.
■520 ▼aMachine learning is becoming prevalent in high energy physics, with numerous applications in physics analyses and event reconstruction showing great improvements compared to traditional computing methods. This thesis studies three projects which each propose new avenues for machine learning applications within the high energy physics CMS experiment located at CERN. In the first project, a search for a dark matter signal called "emerging jets" is performed, using graph neural networks to greatly increase sensitivity to the signal's signature within the data. The result of this dark matter search sets the most stringent exclusion limits to date on theoretical emerging jet models. Motivated by inefficiencies encountered when processing the emerging jet graph neural network at Fermi National Accelerator Laboratory's computing centers, the second project re-optimizes the computing centers for machine learning inference. This re-optimization uses NVIDIA Triton Inference Servers to process users' analysis code heterogeneously, therefore achieving high processing throughput and decreasing user time-to-insight. The last project focuses on an upgrade to the CMS experiment's real-time event selection system which improves physics object reconstruction under harsh processing conditions. A boosted decision tree is used to quickly and efficiently quantify a reconstructed particle's "track quality" in order to remove particle tracks reconstructed erroneously. In summary, this thesis will not only present examples of how high energy physics can greatly benefit by leveraging machine learning techniques for physics analysis and reconstruction, but will also provide guidance on how the field can prepare for the inevitable increase in machine learning applications.
■590 ▼aSchool code: 0051.
■650 4▼aParticle physics
■650 4▼aComputer science
■650 4▼aComputational physics
■653 ▼aDark matter
■653 ▼aHigh energy physics
■653 ▼aLarge hadron collider
■653 ▼aMachine learning
■653 ▼aEmerging jets
■690 ▼a0798
■690 ▼a0984
■690 ▼a0800
■690 ▼a0216
■71020▼aUniversity of Colorado at Boulder▼bPhysics.
■7730 ▼tDissertations Abstracts International▼g85-11B.
■790 ▼a0051
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160850▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
