Документ взят из кэша поисковой машины. Адрес оригинального документа : http://zebu.uoregon.edu/~uochep/talks/talks04/meas.pdf Дата изменения: Tue Dec 14 23:33:45 2004 Дата индексирования: Tue Oct 2 09:54:16 2012 Кодировка: IBM-866 Поисковые слова: п п п п п п п п

Measurement of the Neutralino Stop mass difference
The Search for Dark Matter at an LC - A Status report
Jan Strube, Norman Graf and Michael Peskin
jstrube@darkwing.uoregon.edu

University of Oregon

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Overview
Details of the model Tools First results Summary / Outlook

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LCC 5
mt = 175GeV sign(І) = +1 tan = 30 A0 = -1300 M0 = 600 M
1/2

= 200

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Neutralino Dark Matter
At this point: Neutralino LSP mN1 = (-)81.02 GeV ~ mHL = 122.58 GeV mt = 156.31 GeV ~ mN2 = 161.50 GeV ~ m = (-)162.39 GeV ~

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Simulation
PYTHIA 6.222, ISAJET 7.69, PYSUGI v1.3 as generators tauola 2.5 circe 1.37 with NLC500 for beamstrahlung

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Tools - Software
JAS3 - Java Analysis Studio currently in version 0.7.6 under active development Have ZvTop class from David Jackson AIDA framework for HEP
http://www.freehep.org

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Tools - ZvTop Algorithm
Flavor Tagging Good separation between c and b ZvTop Ver texing Algorithm by Dave Jackson pT -corrected mass Making use of the excellent ver tex resolution of an LC m
p
T

= pT +

i

m

i track

+p

T

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pT -corrected Mass

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Event samples
Signal ~п п ~ ~п ~ e + e - tt c N c N Backgrounds e+ e- Z Z e+ e- Z Z e+ e- Z Z e+ e- cc п cc п bп b п (uu + dd + ss) п п

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Ambitious Approach
Employ full reconstruction and detector simulation Use Preselection Cuts Event Shape Parameters, Missing Energy, 2-Jet Mass Use Flavor Tagging pT -corrected mass Neural Network

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Ambitious Approach
Employ full reconstruction and detector simulation Use Preselection Cuts Event Shape Parameters, Missing Energy, 2-Jet Mass Use Flavor Tagging pT -corrected mass Neural Network ZvTop doesn't work with the track class
ALCPG Victoria LCWS, 2004 н p.10/17


Ambitious Approach
Employ full reconstruction and detector simulation Use Preselection Cuts Event Shape Parameters, Missing Energy, 2-Jet Mass Use Flavor Tagging pT -corrected mass Neural Network Fast MC has issues
ALCPG Victoria LCWS, 2004 н p.10/17


Ambitious Approach
Employ full reconstruction and detector simulation Use Preselection Cuts Event Shape Parameters, Missing Energy, 2-Jet Mass Use Flavor Tagging pT -corrected mass Neural Network Thought we'd get one :-(
ALCPG Victoria LCWS, 2004 н p.10/17


Signal
1. e+ e- Z Z ~ ~п ~ 2. e+ e- tt cN cc п ~ cN пп

Note: This is on the par ton level Hope to do (almost) as well on the Detector level

ALCPG Victoria LCWS, 2004 н p.11/17


First look - quick and dirty
Number of Events / 500 fb- 1 Background: bп: 25930 b cc: 28360 п uds: 8800 Signal: ~п ~ tt: 4580

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Cuts I н 2-jet mass

require 2-jet mass > 97 GeV
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Cuts II н pT -corrected mass

require m

p

T

< 2.0 GeV in both jets
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Cuts III н number of vertices

require number of ver tices < 3 in both jets
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Second look - quick and dirty
Number of Events / 500 fb- 1 Background: bп: 2230 b cc: 3490 п uds: 910 Signal: ~п ~ tt: 3320

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Summary and Outlook
Star ted out motivated Had some drawbacks We have first results The framework is there Willl get real results soon

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