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 |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2013 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Silverstein, Daniel |
|---|---|
| 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 |
|---|
Bibliographic information
| Statement of responsibility | Daniel Silverstein. |
|---|---|
| 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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