Violation in Mixing

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172 Analysis of the time-dependent �È -violating asymmetry in � � � � decays Parameter Fit Result Scaled PRL Æ�� ¦ � ÆÃ� � ¦ � � �Ã� � ¦ � � � ÆÃÃ �� ¦ �� Æ�� ¦ �� Æ�Ã� ¦ �� Ã� � � ¦ � � � Æ�ÃÃ ¦ �� Table 7-5. Results of a fit to the Run 1 data with the new analysis cuts. The PRL results, scaled by the relative efficiency, are shown for comparison. 7.4.2 Comparison with the branching fraction analysis result Table 7-5 shows the new nominal fit results for the Run 1 dataset, including all of the cuts in Table 7-3. For comparison, the expected values, based on the PRL results in Tab. 7-1 and the relative efficiency of the new cuts, are also shown. The likelihood function for this fit is equivalent to the one used in the branching ratio analysis. The relative efficiency is � for signal events and � for background events. 7.5 Background characterization 7.5.1 Composition The sample selected with the cuts described in the previous section contains �� continuum background. The � � purity can be estimated from the fit result in Table 7-5 as Æ�� . The background is again ÕÕ continuum and is made up of � Ù�×, � charm, and tau events. The relative amount of ��, Ã�, and ÃÃ events varies significantly over the different background samples. The charm sample has a much larger fraction of Ã� and ÃÃ decays than Ù�× events due to the dominance of � × decays. The tau sample contains no kaons and is dominated by events where one or both of the tracks come from a � � � decay. These differences affect the relative fraction of ��, Ã�, and ÃÃ background in each tagging category, which we take into account in the maximum likelihood fit. Table 7-6 shows the percentage of events tagged in each category for the different species from fits using the ��Ö�Ò�ÓÚ angle to separate ��, Ã�, and ÃÃ events. For Monte Carlo the results are cross-checked using truth information. The same trends are observed in data and Monte Carlo, with similar absolute tagging efficiencies. As a cross-check on the real data, we include the parameterization obtained from the fit region by floating the tag efficiencies. The fit region and side-band regions are in excellent agreement, giving MARCELLA BONA
7.5 Background characterization 173 �� Sample Lepton Kaon NT1 NT2 Untagged MC truth �� ¦ � �� ¦ � �� ¦ �� ¦ � �� ¦ � MC fit �� ¦ � � ¦ � �� ¦ �� ¦ � �� ¦ � Run 1 � � (FR) � ¦ � �� ¦ �� ¦ �� ¦ �� ¦ � Run 1 � � (SB) � ¦ � �� ¦ �� ¦ � �� ¦ �� ¦ �� Run 1 + 2 � � (SB) � ¦ � �� ¦ �� ¦ � �� ¦ �� ¦ �� Ã� MC truth � ¦ �� ¦ �� ¦ �� ¦ � �� ¦ �� MC fit � ¦ �� ¦ �� ¦ �� ¦ � �� ¦ �� Run 1 � � (FR) � ¦ � � ¦ � �� ¦ �� ¦ � �� ¦ �� Run 1 � � (SB) � ¦ � � ¦ �� ¦ �� ¦ �� ¦ �� Run 1 + 2 � � (SB) � ¦ � � ¦ �� ¦ � �� ¦ �� ¦ �� ÃÃ MC truth � ¦ �� ¦ �� ¦ � �� ¦ �� ¦ � MC fit �� ¦ �� ¦ �� ¦ � � ¦ �� ¦ � Run 1 � � (FR) �� ¦ �� ¦ � �� ¦ �� ¦ � �� ¦ � Run 1 � � (SB) �� ¦ � � ¦ �� ¦ �� ¦ �� ¦ �� Run 1 + 2 � � (SB) �� ¦ � �� ¦ �� ¦ �� ¦ �� ¦ �� “Other” MC truth �� ¦ �� ¦ �� ¦ � � ¦ �� ¦ �� Table 7-6. Tagging efficiencies ( ) within each species for the combined Ù�× + charm + tau Monte Carlo, on-resonance side-band (SB), and on-resonance fit region (FR) samples. The “MC truth” results come from a direct count of events in each category/species using truth information. Events in the “other” category contain tracks not associated with either a pion or kaon. The Run1+2side-band results (highlighted) are used in the final fit. confidence that the higher statistics side-band sample can be used to determine the background tagging efficiencies. We therefore use the results from the combined Run 1 + 2 data in the maximum likelihood fit. 7.5.2 Parameterization of ¡Ø Continuum ÕÕ events typically produce � � � � candidates by combining one high momentum track from each jet. The resulting background ¡Ø distribution Ê�� is not expected to have a significant lifetime 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
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
Page 175 and 176: 7.4 Data samples and event selectio
Page 177: 7.4 Data samples and event selectio
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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