상세정보
Export to Refworks부가기능
Machine Learning Solutions for High Energy Physics: Applications to Electromagnetic Shower Generation, Flavor Tagging, and the Search for di-Higgs Production
상세 프로파일
| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Machine Learning Solutions for High Energy Physics: Applications to Electromagnetic Shower Generation, Flavor Tagging, and the Search for di-Higgs Production. |
| 개인저자 | Paganini, Michela. |
| 단체저자명 | Yale University. Physics. |
| 발행사항 | [S.l.]: Yale University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 417 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-04B. Dissertation Abstract International |
| ISBN | 9781088315378 |
| 학위논문주기 | Thesis (Ph.D.)--Yale University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Advisor: Tipton, Paul L. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | This thesis demonstrate the efficacy of designing and developing machine learning algorithms to selected use cases that encompass many of the outstanding challenges in the field of experimental high energy physics.Although simple implementations of neural networks and boosted decision trees have been used in high energy physics for a long time, the field of machine learning has quickly evolved by devising more complex, fast and stable implementations of learning algorithms. The complexity and power of state-of-the-art deep learning far exceeds those of the learning algorithms implemented in the CERN-developed ROOT library.All aspects of experimental high energy physics have been and will continue being revolutionized by the software- and hardware-based technological advances spearheaded by both academic and industrial research in other technical disciplines, and the emergent trend of increased interdisciplinarity will soon reframe many scientific domains. This thesis exemplifies this spirit of versatility and multidisciplinarity by bridging the gap between machine learning and particle physics, and exploring original lines of work to modernize the reconstruction, particle identification, simulation, and analysis workflows.This contribution documents a collection of novel approaches to augment traditional domain-specific methods with modern, automated techniques based on industry-standard, open-source libraries. Specifically, it contributes to setting the state-of-the-art for impact parameter-based flavor tagging and di-Higgs searches in the 款款bb channel with the ATLAS detector at the LHC, it introduces and lays the foundations for the use of generative adversarial networks for the simulation of particle showers in calorimeters.These results substantiate the notion of machine learning powering particle physics in the upcoming years and establish baselines for future applications. |
| 일반주제명 | Particle physics. Artificial intelligence. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
