東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Machine Learning Solutions for High ...

Paganini, Michela.

Linked to FindBook

Google Book

Amazon

博客來

Machine Learning Solutions for High Energy Physics: Applications to Electromagnetic Shower Generation, Flavor Tagging, and the Search for di-Higgs Production.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning Solutions for High Energy Physics: Applications to Electromagnetic Shower Generation, Flavor Tagging, and the Search for di-Higgs Production./
Author:	Paganini, Michela.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	417 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Contained By:	Dissertations Abstracts International81-04B.
Subject:	Particle physics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13809021
ISBN:	9781088315378

Machine Learning Solutions for High Energy Physics: Applications to Electromagnetic Shower Generation, Flavor Tagging, and the Search for di-Higgs Production.
Paganini, Michela.

Machine Learning Solutions for High Energy Physics: Applications to Electromagnetic Shower Generation, Flavor Tagging, and the Search for di-Higgs Production. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 417 p.

Source: Dissertations Abstracts International, Volume: 81-04, Section: B.

Thesis (Ph.D.)--Yale University, 2019.

This item must not be sold to any third party vendors.

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 γγbb 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.

ISBN: 9781088315378Subjects--Topical Terms:

3433269
Particle physics.

Machine Learning Solutions for High Energy Physics: Applications to Electromagnetic Shower Generation, Flavor Tagging, and the Search for di-Higgs Production.
LDR:02964nmm a2200301 4500 001 2265283
005 20200514111944.5
008 220629s2019 ||||||||||||||||| ||eng d
020 $a 9781088315378
035 $a (MiAaPQ)AAI13809021
035 $a AAI13809021
040 $a MiAaPQ $c MiAaPQ
100 1 $a Paganini, Michela. $3 3542437
245 1 0 $a Machine Learning Solutions for High Energy Physics: Applications to Electromagnetic Shower Generation, Flavor Tagging, and the Search for di-Higgs Production.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 417 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
500 $a Advisor: Tipton, Paul L.
502 $a Thesis (Ph.D.)--Yale University, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a 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 γγbb 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.
590 $a School code: 0265.
650 4 $a Particle physics. $3 3433269
650 4 $a Artificial intelligence. $3 516317
690 $a 0798
690 $a 0800
710 2 $a Yale University. $b Physics. $3 2101654
773 0 $t Dissertations Abstracts International $g 81-04B.
790 $a 0265
791 $a Ph.D.
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13809021