- author = "Kyle S. Cranmer",
- title = "Searching for New Physics: Contributions to {LEP} and the {LHC}",
- school = "University of Wisconsin-Madison",
- year = "2005",
- address = "USA",
- month = "01 " # nov,
- keywords = "genetic algorithms, genetic programming, PhysicsGP",
-
URL = "
http://www.theoryandpractice.org/kyle/Files/cranmer_thesis.pdf",
-
URL = "
https://cds.cern.ch/record/823591",
- size = "233 pages",
- abstract = "This dissertation is divided into two parts and consists of a series of contributions to searches for new physics with LEP and the LHC. In the first part, an exhaustive comparison of ALEPH's LEP2 data and Standard Model predictions is made for several hundred final states. The observations are in agreement with predictions with the exception of the e- mu+ final state. Using the same general purpose particle identification procedure, searches for minimal supergravity signatures, excited electrons, doubly charged Higgs bosons, singly charged Higgs bosons, and the Standard Model Higgs boson were performed. The results of those searches are in agreement with previous ALEPH analyses. The second part focuses on preparation for searches for Higgs bosons with masses between 100 and 200 GeV. Improvements to the relevant Monte Carlo generators and the reconstruction of missing transverse momentum are presented. A detailed full simulation study of Vector Boson Fusion Higgs decaying to tau leptons confirms the qualitative conclusion that the channel is powerful near the LEP limit. Several novel statistical and multivariate analysis algorithms are considered, and their impact on Higgs searches is assessed. Finally, sensitivity estimates are provided for the combination of channels available for low mass Higgs searches. With 30 fb^-1 the expected ATLAS sensitivity is above five sigma for Higgs masses above 105 GeV.",
-
notes = "'I would like to use one page to point out that if
this dissertation was not double-spaced, then it would
be fifty pages shorter. This is practice is silly and
antiquated in the era of LATEX typesetting.'
'In total, three multivariate analyses were performed:
a Neural Network analysis using back-propagation with momentum,
a Support Vector Regression analysis using Radial Basis Functions, and
a Genetic Programming analysis using the software described in Appendix E.'
Copyright by Kyle S. Cranmer 2005 Some Rights Reserved This work is licensed under the Creative Commons Attribution-ShareAlike License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/2.0/ or send a letter to Creative Commons, 559 Nathan Abbott Way, Stanford, California 94305, USA.
https://cds.cern.ch/record/823591/files/CERN%20THESIS%202005%20011.pdf
Supervisor: Wu Sau Lan
Also known as \cite{Cranmer:823591}",
