Violation in Mixing

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124 Measurement of Branching Fractions for � ¦ � Ã � ¦ decays number of Ks candidates in continuum MC / 0.75 MeV 1600 1400 1200 1000 800 600 400 200 number of ks candidates in MC signal events / 0.75 MeV 600 500 400 300 200 100 ALLCHAN 5009. 38.00 / 52 P1 11.11 9.979 P2 -16.14 20.01 P3 4786. 71.99 P4 0.4981 0.5025E-04 P5 0.1899E-02 0.6618E-04 P6 0.6298 0.3218E-01 P7 0.4981 0.1459E-03 P8 0.4739E-02 0.2104E-03 0 0.475 0.48 0.485 0.49 0.495 0.5 0.505 0.51 0.515 0.52 reconstructed Ks invariant mass (GeV) Figure 5-3. Ã Ë invariant mass in � � Ã Ë � Monte Carlo simulated data. ALLCHAN 0.3682E+05 41.84 / 52 P1 48.95 114.6 P2 801.4 230.4 P3 9916. 275.4 P4 0.4981 0.5110E-04 P5 0.1934E-02 0.7739E-04 P6 0.6106 0.3850E-01 P7 0.4979 0.3144E-03 P8 0.5517E-02 0.5841E-03 0 0.475 0.48 0.485 0.49 0.495 0.5 0.505 0.51 0.515 0.52 recontructed Ks invariant mass (GeV) number of ks candidates in offres data / 0.75 MeV 1000 800 600 400 200 ALLCHAN 0.2271E+05 57.32 / 52 P1 42.65 97.89 P2 463.5 195.2 P3 6262. 220.6 P4 0.4977 0.8313E-04 P5 0.2262E-02 0.1297E-03 P6 0.6021 0.6275E-01 P7 0.4966 0.6031E-03 P8 0.5738E-02 0.7447E-03 0 0.475 0.48 0.485 0.49 0.495 0.5 0.505 0.51 0.515 0.52 reconstructed Ks invariant mass (GeV) Figure 5-4. Ã Ë invariant mass in continuum Monte Carlo (left) and off-resonance data (right). MARCELLA BONA
5.3 Analysis Strategy 125 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 0 5 10 15 20 25 30 35 40 45 50 ks candidate lifetime significance 15 12.5 10 7.5 5 2.5 0 -2.5 0 20 40 60 80 100 Kshort lifetime significance Figure 5-5. The variable ØÃË��Ø distributions. Left plot: lifetime significance for fake (histogram) and ÃË true (dots) ÃË continuum Monte Carlo data. Right plot: Data-MC agreement in Â��Ã Ë events in data (dots) and in the Â��ÃË signal MC (histogram). The plots are background subtracted (Ã Ë mass side-bands) and the histograms are normalized to equal area. Since Ã Ë’s in �� events have typically lower momenta with respect to Ã Ë in charmless two-body decays,, we have also used, as a control sample for our channel, a sample of reconstructed Â��Ã Ë events: the momentum spectrum of these Ã Ë goes from 1 GeV up to 3 GeV. Right plot in figure 5-5 shows the good agreement of the lifetime significance distribution for data and MC in this control sample. The statistical significance Ë � Ë � (Ë and � are the number of expected signal and background events respectively) of a cut on ØÃË��Ø ÃË is shown in Fig. 5-6. For this exercise a branching fraction of �Ê � � � Ã Ë � ��¢ � is assumed. From this optimization, we require Ø ÃË ��Ø ÃË � �: the uncertainty on this cut will be taken into account in the systematic study, where we vary the lifetime significance cut and calculate from the global likelihood fit the difference of the yields found with respect to the nominal results (see Sec. 5.4.5). 5.3 Analysis Strategy Signal yields in � � Ã Ë � and � � Ã Ë Ã channels are determined using an unbinned maximum likelihood technique. The background suppression variables and parameterization of the probability density functions (PDFs) are discussed in Sec. 5.4. The results of a fit to the full Run1 data-set are presented in Sec. 5.5. As a crosscheck, background suppression and particle identification cuts have been applied to 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
Page 79 and 80: Efficiency 2.2 The BABAR detector.
Page 81 and 82: 2.2 The BABAR detector. 75 Table 2-
Page 83 and 84: 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: 5.2 Ã Ë reconstruction 123 1 0.8
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 and 178: 7.4 Data samples and event selectio
Page 179 and 180: 7.5 Background characterization 173
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7.5 Background characterization 175
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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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