Violation in Mixing

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130 Measurement of Branching Fractions for � ¦ � Ã � ¦ decays 400 350 300 250 200 150 100 50 0 -4 -3 -2 -1 0 1 2 3 4 fisher variable (area normalization) 450 400 350 300 250 200 150 100 50 0 -4 -3 -2 -1 0 1 2 3 4 fisher variable (area normalization) Figure 5-11. Fisher discriminant output for continuum Monte Carlo superimposed on the on-resonance distribution of the Fisher variable (left) and off-resonance data compared to on-resonance (right). These distributions correspond to the Ñ�Ë side-band (Ñ�Ë � �� Î). 450 400 350 300 250 200 150 100 50 ALLCHAN 2624. 24.22 / 17 P1 288.9 35.65 P2 0.4626E-01 0.3085E-01 P3 0.2868 0.1593E-01 P4 134.3 32.50 P5 0.5069 0.8207E-01 P6 0.4132 0.2227E-01 0 -4 -3 -2 -1 0 1 2 3 4 fisher variable 1000 800 600 400 200 ALLCHAN 6531. 27.71 / 19 P1 6504. 80.65 P2 0.9609E-02 0.1476 P3 0.4625 0.4007E-01 P4 0.1717 0.8189E-01 P5 -0.3669 0.1392E-01 P6 0.3319 0.1021E-01 0 -4 -3 -2 -1 0 1 2 3 4 fisher variable in kspi signal MC Figure 5-12. Left plot shows the Fisher output comparison: on the left side, signal Ã Ë � MC with the signal � � MC (black dots) superimposed, on the right side the on-resonance Ñ �Ë side-band with the parameterization for background events used in the global likelihood fit. Right plot shows the Fisher parameterization for signal events: signal Ã Ë � MC is fitted. MARCELLA BONA
5.4 Background Suppression and PDF Parameterization 131 5.4.5 Efficiency In summary, after the standard selection, the following cuts are applied: ¯ �ÑÃË ÑÈ�� Å�Î� ¯ Ø ÃË ��Ø ÃË ¯ PID cuts � � Monte Carlo is used to estimate the efficiency of different cuts with the exceptions of track reconstruction, the Ã Ë reconstruction and the Ã Ë invariant mass cut. In this last case, we consider the efficiency (and the relative error) found in the � � Ã Ë � (see Section 5.2). Table 5-2 summarizes the efficiencies of the different cuts. To correct the MC final efficiency for the tracking uncertainty (see Sec. 4.5), we use the results from detailed studies on various control samples [61]: the correction factor comes out to be �ØÖ� � �� with an uncertainty of � per track. In the Ã Ë case, the correction on the efficiency is done on the base of the correction tables from inclusive Ã Ë reconstruction described in Sec. 3.3.4: the correction factor is � � ¦ � �. No correction is applied for the Ã Ë daughter tracks since they are taken from the list ChargedTracks and the Tracking Efficiency Working Group has found agreement with MC, but the uncertainty per Ã Ë ( per ChargedTracks track) has to be taken into account to evaluate the error on the efficiency. Table 5-2. Efficiencies of the cuts for the decay mode � � Ã Ë � as determined from signal MC only. Cut Efficiency Ã Ë � Efficiency Ã Ë Ã reco + tag bits + Ê + Sph + GoodTracksAccLoose �� Ó× �Ë� � �� ÑÃË ÑÈ�� Å�Î �� Ø ÃË ��Ø ÃË � � �� proton veto �� Ò � � �� all previous cuts � � �� MEASUREMENT OF BRANCHING FRACTIONS FOR � ¦ � Ã � ¦ 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
Page 85 and 86: 3.3 Studies on data 79 19.5° BINS
Page 87 and 88: 3.3 Studies on data 81 ¯ off-reson
Page 89 and 90: 3.3 Studies on data 83 0.504 0.502
Page 91 and 92: 3.3 Studies on data 85 0.03 0.025 0
Page 95 and 96: 3.3 Studies on data 89 N(π + π -
Page 99 and 100: 3.3 Studies on data 93 Figure 3-13.
Page 103 and 104: 4 Strategy and Tools for Charmless
Page 105 and 106: 4.2 Event selection 99 channel sele
Page 107 and 108: 4.2 Event selection 101 ÀÐ �
Page 109 and 110: 4.2 Event selection 103 Figure 4-2.
Page 111 and 112: 4.3 Background fighting 105 Figure
Page 113 and 114: 4.4 PID selection 107 In the case o
Page 115 and 116: 4.4 PID selection 109 θ c Cut Effi
Page 117 and 118: 4.5 Tracking Corrections 111 Ë�
Page 119 and 120: 4.6 Analysis methods 113 Ñ�Ë si
Page 121 and 122: 4.6 Analysis methods 115 Events/2.5
Page 123 and 124: 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 135: 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 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
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7.7 The maximum likelihood analysis
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7.7 The maximum likelihood analysis
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7.8 Validation studies 185 Figure 7
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7.8 Validation studies 187 100 75 5
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7.8 Validation studies 189 Figure 7
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7.8 Validation studies 191 Paramete
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7.9 Results 193 Parameter Fit Resul
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7.9 Results 195 Events/2 MeV/c 2 Ev
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7.10 Systematic studies 197 Categor
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7.11 Summary 199 �Ã� Ë��
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7.11 Summary 201 �Ã� Ë��
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7.11 Summary 203 Variation �Ã�
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BIBLIOGRAFIA 205 Bibliografia Chapt
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BIBLIOGRAFIA 207 [38] J. Charles, P
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BIBLIOGRAFIA 209 Chapter 7 [67] D.
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Violation in Mixing

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