Violation in Mixing

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170 Analysis of the time-dependent �È -violating asymmetry in � � � � decays Candidates per 2.0 MeV/c 2 30 20 10 Run 1 Run 2 Total Category � � Tot � � Tot � � Tot Lepton � �� Kaon � �� NT1 � �� NT2 � �� untagged – – – – � � – – �� Total � � �� Table 7-4. Event yields in Run 1 and Run 2 separated by tag flavor and category. Candidates per 2.0 MeV/c 2 20 15 10 5 BABAR 0 5.2 5.225 5.25 5.275 5.3 GeV/c mES (Lepton) 2 BABAR 0 5.2 5.225 5.25 mES (NT1) 5.275 5.3 GeV/c 2 Candidates per 2.0 MeV/c 2 60 40 20 Candidates per 2.0 MeV/c 2 0 5.2 5.225 5.25 mES (NT2) 5.275 5.3 GeV/c 2 80 60 40 20 BABAR 0 5.2 5.225 5.25 mES (Kaon) 5.275 5.3 GeV/c 2 BABAR Candidates per 2.0 MeV/c 2 150 100 50 0 5.2 5.225 5.25 5.275 5.3 GeV/c mES (Untagged) 2 Figure 7-1. Distributions of Ñ�Ë in the Run 1 + 2 dataset for events in different tagging categories and for the subset of untagged events. MARCELLA BONA
7.4 Data samples and event selection 171 Figure 7-2. The fitted �� yield divided by its error for three samples of 1000 toy experiments corresponding to different cuts on Ó× �Ë. The separation between signal and background in the Fisher variable is reduced when cutting harder on � Ó× �Ë�, which partially compensates the increased signal-to-background ratio. To optimize this cut, toy experiments were generated corresponding to each of the three samples. Each experiment is fit with the likelihood function used in the branching ratio analysis with the usual PDFs for Ñ�Ë, ¡�, and � and the appropriate re-parameterized Fisher PDF based on the � Ó× �Ë� cut. Events are generated with Poisson statistics corresponding to the published Run 1 result (see Sec. 7.2), scaled up to fb and modified by the relative efficiency of the different Ó× �Ë cuts. The signal efficiency is obtained assuming a flat distribution, while the background efficiency is obtained from the on-resonance Ñ�Ë side-band data. Figure 7-2 shows the distribution of fitted �� yield divided by its error, which is an estimate of Ë� Ô Ë �, for each of the toy samples. The �� and �� samples give the same significance, while the �� sample is less optimal. The cut at �� removes � of the background and leads to more Gaussian Fisher distributions. The reduction in background means that the time-dependent fits and toy Monte Carlo studies run twice as fast, which is not an insignificant advantage. We have therefore decided to use � Ó× �Ë� � �� as our default cut. ANALYSIS OF THE TIME-DEPENDENT �È -VIOLATING ASYMMETRY IN � � � � DECAYS
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ÍÒ�Ú�Ö×�Ø��
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Contents Introduction . ...........
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4 Strategy and Tools for Charmless
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7.2 Branching fraction � � �
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Introduction The main goal of the B
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1 �È Violation in the �� Sys
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1.2 Neutral � Mesons 7 Note, howe
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1.2 Neutral � Mesons 9 Applying t
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1.2 Neutral � Mesons 11 �È �
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1.2 Neutral � Mesons 13 ��
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1.2 Neutral � Mesons 15 � ¡�
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1.2 Neutral � Mesons 17 and � a
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1.2 Neutral � Mesons 19 given tim
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1.3 The Three Types of �È Violat
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1.4 �È Violation in the Standard
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1.5 Determination of « 37 1.5 Dete
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1.5 Determination of « 39 Assuming
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1.5 Determination of « 41 Figure 1
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1.5 Determination of « 43 b q (a)
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1.5 Determination of « 45 � �
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2 The BABAR Experiment 2.1 Physics
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2.1 Physics at � � B Factory op
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2.2 The BABAR detector. 51 Integrat
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2.2 The BABAR detector. 53 The dete
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2.2 The BABAR detector. 55 two oute
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2.2 The BABAR detector. 57 SVT Effi
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2.2 The BABAR detector. 59 324 1015
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Efficiency 2.2 The BABAR detector.
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2.2 The BABAR detector. 63 2.2.4 Th
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entries per mrad 2.2 The BABAR dete
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2.2 The BABAR detector. 67 entries
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2.2 The BABAR detector. 69 energies
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2.2 The BABAR detector. 71 The main
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Efficiency 2.2 The BABAR detector.
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2.2 The BABAR detector. 75 Table 2-
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3 Ã Ë reconstruction and efficien
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3.3 Studies on data 79 19.5° BINS
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3.3 Studies on data 81 ¯ off-reson
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3.3 Studies on data 83 0.504 0.502
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3.3 Studies on data 85 0.03 0.025 0
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3.3 Studies on data 87 0.504 0.502
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3.3 Studies on data 89 N(π + π -
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3.3 Studies on data 91 0.508 0.506
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3.3 Studies on data 93 Figure 3-13.
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3.3 Studies on data 95 0.508 0.506
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4 Strategy and Tools for Charmless
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4.2 Event selection 99 channel sele
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4.2 Event selection 101 ÀÐ �
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4.2 Event selection 103 Figure 4-2.
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4.3 Background fighting 105 Figure
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4.4 PID selection 107 In the case o
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4.4 PID selection 109 θ c Cut Effi
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4.5 Tracking Corrections 111 Ë�
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4.6 Analysis methods 113 Ñ�Ë si
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4.6 Analysis methods 115 Events/2.5
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4.6 Analysis methods 117 Figure 4-1
Page 125 and 126: 4.6 Analysis methods 119 θ c radia
Page 127 and 128: 5 Measurement of Branching Fraction
Page 129 and 130: 5.2 Ã Ë reconstruction 123 1 0.8
Page 131 and 132: 5.3 Analysis Strategy 125 0.35 0.3
Page 133 and 134: 5.4 Background Suppression and PDF
Page 139 and 140: 5.5 Maximum likelihood analysis 133
Page 151 and 152: 5.6 Counting analysis 145 Table 5-1
Page 153 and 154: 5.7 Determination of branching frac
Page 155 and 156: 6 Measurement of Branching Fraction
Page 157 and 158: 6.3 The maximum likelihood analysis
Page 163 and 164: 6.4 Counting analysis 157 120 100 8
Page 165 and 166: 6.5 Analysis choice 159 6.5 Analysi
Page 167 and 168: 6.6 Results on the Run1 data-set 16
Page 169 and 170: 6.7 Determination of the branching
Page 171 and 172: 7 Analysis of the time-dependent
Page 173 and 174: 7.3 Analysis strategy 167 Parameter
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Page 179 and 180: 7.5 Background characterization 173
Page 181 and 182: 7.5 Background characterization 175
Page 191 and 192: 7.8 Validation studies 185 Figure 7
Page 193 and 194: 7.8 Validation studies 187 100 75 5
Page 195 and 196: 7.8 Validation studies 189 Figure 7
Page 197 and 198: 7.8 Validation studies 191 Paramete
Page 199 and 200: 7.9 Results 193 Parameter Fit Resul
Page 201 and 202: 7.9 Results 195 Events/2 MeV/c 2 Ev
Page 203 and 204: 7.10 Systematic studies 197 Categor
Page 205 and 206: 7.11 Summary 199 �Ã� Ë��
Page 207 and 208: 7.11 Summary 201 �Ã� Ë��
Page 209 and 210: 7.11 Summary 203 Variation �Ã�
Page 211 and 212: BIBLIOGRAFIA 205 Bibliografia Chapt
Page 213 and 214: BIBLIOGRAFIA 207 [38] J. Charles, P
Page 215 and 216: BIBLIOGRAFIA 209 Chapter 7 [67] D.
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Violation in Mixing

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