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A Deep-Learning-Based Muon Neutrino CCQE Selection for Searches Beyond the Standard Model with MicroBooNe.
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A Deep-Learning-Based Muon Neutrino CCQE Selection for Searches Beyond the Standard Model with MicroBooNe.
자료유형  
 학위논문
Control Number  
0016053144
International Standard Book Number  
9798534649703
Dewey Decimal Classification Number  
593.7
Main Entry-Personal Name  
Cianci, Davio.
Publication, Distribution, etc. (Imprint  
[S.l.] : Columbia University., 2021
Publication, Distribution, etc. (Imprint  
Ann Arbor : ProQuest Dissertations & Theses, 2021
Physical Description  
250 p.
General Note  
Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
General Note  
Advisor: Karagiorgi, Georgia.
Dissertation Note  
Thesis (Ph.D.)--Columbia University, 2021.
Restrictions on Access Note  
This item must not be sold to any third party vendors.
Subject Added Entry-Topical Term  
Particle physics.
Subject Added Entry-Topical Term  
Physics.
Subject Added Entry-Topical Term  
Simulation.
Subject Added Entry-Topical Term  
Energy.
Subject Added Entry-Topical Term  
Hypothesis testing.
Subject Added Entry-Topical Term  
Hypotheses.
Subject Added Entry-Topical Term  
Experiments.
Subject Added Entry-Topical Term  
Sensors.
Index Term-Uncontrolled  
Deep learning
Index Term-Uncontrolled  
Low energy excess
Index Term-Uncontrolled  
Liquid argon time projection chamber
Added Entry-Corporate Name  
Columbia University Physics
Host Item Entry  
Dissertations Abstracts International. 83-02B.
Host Item Entry  
Dissertation Abstract International
Electronic Location and Access  
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Control Number  
joongbu:609842
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