Violation in Mixing

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192 Analysis of the time-dependent �È -violating asymmetry in � � � � decays Sample � ¡Ñ�� Ë�� (blind) �� (blind) Run1 �� ¦ � � �� ¦ � �� ¦ �� ¦ �� Run2 �� ¦ � �� ¦ � � �� ¦ � � �� ¦ �� Run1 + Run2 �� ¦ � �� ¦ � �� ¦ �� ¦ �� Table 7-19. Summary of fits floating �, ¡Ñ��, and the blinded �È asymmetries Ë�� and ��. Units are Ô× for � and �� Ô× for ¡Ñ��. -2 (log L - log L 0 ) 7.9 Results 4 3 2 1 BABAR 0 -1 -0.5 0 0.5 1 Sππ -2 (log L - log L 0 ) 4 2 0 BABAR -1 -0.5 0 0.5 Cππ Figure 7-15. Scans of the likelihood function vs. Ë ��, ��, and �Ã�. -2 (log L - log L 0 ) 25 20 15 10 5 BABAR 0 -1 -0.5 0 0.5 1 ACP The unblinded fit results are shown in Tab. 7-20 Figure 7-15 shows scans of the likelihood function with respect to the �È parameters. To estimate how likely the error obtained on the full dataset is, we generated �� toy experiments with yields given by the data fit (no Poisson fluctuations). Figure 7-16 shows the pull distributions for Ë�� and ��. Figure 7-17 shows the error distribution from the ensemble of toy experiments, with the data results indicated by the arrows. Figure 7-18 shows distributions of Ñ�Ë and ¡� for events enhanced in signal �� and Ã� decays using likelihood ratio cuts. The curves represent projections of the fit result scaled by the efficiency of the additional cuts. Figure 7-19 shows the ¡Ø distribution for ��-selected events, with a looser selection than the one applied in Fig. 7-18. We find that the background resolution function describes the tails of the ¡Ø distribution well, and the core is consistent with � decay. 7.9.1 Cross-checks Table 7-21 summarizes several tests to cross-check the stability of the result. We fit separately the Run 1 and Run 2 datasets and find the weighted averages are consistent with the results for the entire dataset. We also fit the � and � tag samples separately, again with consistent results. To test the stability of the fit MARCELLA BONA
7.9 Results 193 Parameter Fit Result Æ�� ÆÃ� �� Ã� � � ¦ � � ÆÃÃ Æ�� Æ�Ã� �� ¦ �� Ã� � ¦ � Æ�ÃÃ � � � ¡�Ô � � ¡�Ô �� ¦ � �� ¦ � �� Ë�� Corr(Ë��,��) Table 7-20. Summary of the unblinded fit results on the Run 1 + 2 dataset. The last row gives the correlation between the time-dependent �È asymmetry parameters. � � 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
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4.6 Analysis methods 119 θ c radia
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5 Measurement of Branching Fraction
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5.2 Ã Ë reconstruction 123 1 0.8
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5.3 Analysis Strategy 125 0.35 0.3
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5.4 Background Suppression and PDF
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5.5 Maximum likelihood analysis 133
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Page 147 and 148: 5.5 Maximum likelihood analysis 141
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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
Page 175 and 176: 7.4 Data samples and event selectio
Page 177 and 178: 7.4 Data samples and event selectio
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: 7.8 Validation studies 191 Paramete
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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