Machine learning in the search for dark matter with the ATLAS detector
Abstract/Contents
- Abstract
- The results of a search for pair production of the scalar partners of the bottom quarks in 4.7 fb1 of pp collisions at ps = 7 TeV using the ATLAS detector at the LHC are reported. Scalar bottoms are searched for in events with large missing transverse momentum and two jets identified as originating from a b-quark in the final state. In an R-parity conserving minimal supersymmetric scenario, assuming that the scalar bottom decays exclusively into a bottom quark and a neutralino, 95% confidence-level upper limits are obtained in the b1 c10 mass plane. Exclusions are first calculated using the frequentist method. For c 10 masses less than 150 GeV, b1 masses up to 500 GeV are excluded. The exclusion is extended up to 600 GeV by the use of machine learning methods.
Description
Type of resource | text |
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Form | electronic; electronic resource; remote |
Extent | 1 online resource. |
Publication date | 2013 |
Issuance | monographic |
Language | English |
Creators/Contributors
Associated with | Silverstein, Daniel | |
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Associated with | Stanford University, Department of Physics. | |
Primary advisor | Su, Dong | |
Thesis advisor | Su, Dong | |
Thesis advisor | Burchat, P. (Patricia) | |
Thesis advisor | Wacker, Jay | |
Advisor | Burchat, P. (Patricia) | |
Advisor | Wacker, Jay |
Subjects
Genre | Theses |
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Bibliographic information
Statement of responsibility | Daniel Silverstein. |
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Note | Submitted to the Department of Physics. |
Thesis | Thesis (Ph.D.)--Stanford University, 2013. |
Location | electronic resource |
Access conditions
- Copyright
- © 2013 by Daniel Silverstein
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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