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Search for Dark Matter With the ATLAS Detector and Development of a Track Reconstruction Algorithm for the ATLAS Inner Tracker
Search for Dark Matter With the ATLAS Detector and Development of a Track Reconstruction Algorithm for the ATLAS Inner Tracker
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105637
- ISBN
- 9798265471253
- DDC
- 530
- 저자명
- Pham, Minh-Tuan.
- 서명/저자
- Search for Dark Matter With the ATLAS Detector and Development of a Track Reconstruction Algorithm for the ATLAS Inner Tracker
- 발행사항
- [Sl] : The University of Wisconsin - Madison, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 257 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-06, Section: B.
- 주기사항
- Advisor: Wu, Sau Lan.
- 학위논문주기
- Thesis (Ph.D.)--The University of Wisconsin - Madison, 2025.
- 초록/해제
- 요약This thesis is divided into two main parts. The first part presents a summary of dark matter searches performed by the ATLAS experiment and a statistical combination of the three most sensitive analyses. The results are interpreted within the framework of a Two-Higgs-Doublet Model extended by a pseudoscalar mediator (2HDM+a). These analyses are based on 139 fb-1 of proton-proton collision data collected at a center-of-mass energy of 13 TeV during Run 2 of the LHC. The combined analyses target final states involving large missing transverse energy and a visible signature from the decay of a Standard Model Higgs boson or Z boson, as well as processes involving the production of charged Higgs bosons. This work provides the most comprehensive set of constraints on the 2HDM+a model published by ATLAS to date.The second part focuses on the reconstruction of charged-particle tracks in the ATLAS Inner Tracker (ITk), which is confronted with the extreme pile-up conditions expected in the High-Luminosity phase of the Large Hadron Collider (HL-LHC). Given the anticipated increase in instantaneous luminosity and associated event complexity-resulting in up to 200 simultaneous interactions per bunch crossing-traditional reconstruction algorithms face significant computational challenges. To address this, a novel track reconstruction algorithm based on Graph Neural Networks (GNNs) has been developed and evaluated. Using full detector simulation data on realistic ITk geometry, we demonstrate competitive physics performance of the GNN-based tracking approach with respect to the current tracking algorithm. The computational efficiency is optimized and measured in detail. This approach shows significant potential for efficient pattern recognition in dense detector environments, leveraging modern hardware accelerators such as GPUs and FPGAs for fast and scalable event reconstruction.
- 일반주제명
- Physics
- 일반주제명
- Astrophysics
- 일반주제명
- Particle physics
- 일반주제명
- Computational physics
- 키워드
- ATLAS detector
- 키워드
- Dark matter
- 키워드
- Inner tracker
- 기타저자
- The University of Wisconsin - Madison Physics
- 기본자료저록
- Dissertations Abstracts International. 87-06B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008260126s2025 us c eng d■001000017360916
■00520260202105637
■006m o d
■007cr#unu||||||||
■020 ▼a9798265471253
■035 ▼a(MiAaPQ)AAI32395388
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a530
■1001 ▼aPham, Minh-Tuan.
■24510▼aSearch for Dark Matter With the ATLAS Detector and Development of a Track Reconstruction Algorithm for the ATLAS Inner Tracker
■260 ▼a[Sl]▼bThe University of Wisconsin - Madison▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a257 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-06, Section: B.
■500 ▼aAdvisor: Wu, Sau Lan.
■5021 ▼aThesis (Ph.D.)--The University of Wisconsin - Madison, 2025.
■520 ▼aThis thesis is divided into two main parts. The first part presents a summary of dark matter searches performed by the ATLAS experiment and a statistical combination of the three most sensitive analyses. The results are interpreted within the framework of a Two-Higgs-Doublet Model extended by a pseudoscalar mediator (2HDM+a). These analyses are based on 139 fb-1 of proton-proton collision data collected at a center-of-mass energy of 13 TeV during Run 2 of the LHC. The combined analyses target final states involving large missing transverse energy and a visible signature from the decay of a Standard Model Higgs boson or Z boson, as well as processes involving the production of charged Higgs bosons. This work provides the most comprehensive set of constraints on the 2HDM+a model published by ATLAS to date.The second part focuses on the reconstruction of charged-particle tracks in the ATLAS Inner Tracker (ITk), which is confronted with the extreme pile-up conditions expected in the High-Luminosity phase of the Large Hadron Collider (HL-LHC). Given the anticipated increase in instantaneous luminosity and associated event complexity-resulting in up to 200 simultaneous interactions per bunch crossing-traditional reconstruction algorithms face significant computational challenges. To address this, a novel track reconstruction algorithm based on Graph Neural Networks (GNNs) has been developed and evaluated. Using full detector simulation data on realistic ITk geometry, we demonstrate competitive physics performance of the GNN-based tracking approach with respect to the current tracking algorithm. The computational efficiency is optimized and measured in detail. This approach shows significant potential for efficient pattern recognition in dense detector environments, leveraging modern hardware accelerators such as GPUs and FPGAs for fast and scalable event reconstruction.
■590 ▼aSchool code: 0262.
■650 4▼aPhysics
■650 4▼aAstrophysics
■650 4▼aParticle physics
■650 4▼aComputational physics
■653 ▼aATLAS detector
■653 ▼aDark matter
■653 ▼aInner tracker
■653 ▼aGraph Neural Networks
■653 ▼aLarge Hadron Collider
■690 ▼a0605
■690 ▼a0596
■690 ▼a0798
■690 ▼a0216
■71020▼aThe University of Wisconsin - Madison▼bPhysics.
■7730 ▼tDissertations Abstracts International▼g87-06B.
■790 ▼a0262
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17360916▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
