Violation in Mixing

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174 Analysis of the time-dependent �È -violating asymmetry in � � � � decays Parameter Run 1 Run 2 MC � ÓÖ� � � ¦ � � � ¦ � � � � ¦ � � � ÓÖ� �� ¦ � �� ¦ � � �� ¦ � �Ø��Ð � �� ¦ � � � �� ¦ � � � � ¦ � �Ø��Ð � � ¦ � � � ¦ � � � � ¦ � �� Ø��Ð �� ¦ � � �� ¦ � �� ¦ � � �ÓÙØ � �� ¦ � � � �� ¦ � � � ¦ � � �ÓÙØ (fixed) � (fixed) � (fixed) �ÓÙØ �� ¦ � �� ¦ � �� ¦ �� Table 7-7. Parameters for the triple-Gaussian background ¡Ø resolution function Ê �� in on-resonance and continuum Monte Carlo samples. Separate parameterizations are used for Run 1 and Run 2. Means and resolutions are in Ô×. component, even for events. We therefore choose to parameterize the distribution as the sum of three Gaussians: core, tail, and outlier, with the same parameters for ��, Ã�, and ÃÃ background. Attempting to incorporate event-by-event errors leads to unstable parameterizations, therefore, we do not make use of the ¡Ø error. The Gaussian parameters are obtained from an unbinned maximum likelihood fit to the on-resonance sideband sample, with the outlier mean fixed to Ô×. Figure 7-3 shows the fit result for Run 1 and Run 2 data. Table 7-7 lists the fitted parameters. The � �Ò�Ó� for the triple-Gaussian fit is �� in Run 1 and �� in Run 2. The Kolmogorov test returns a probability of �� for both Run 1 and Run 2. There is a significant negative bias in �Ø��Ð in Run 1 and a positive bias in Run 2. The origin of the shifts is not understood, but it is accounted for in the systematic error (Sec. 7.10). Several cross-checks have been performed to validate the ¡Ø parameterization. Figure 7-4 shows the onresonance side-band data compared to a parameterization obtained in the fit region. In this fit the signal and background yields are determined from Ñ�Ë, ¡�, �, and � simultaneously with the background ¡Ø parameters. For the signal, we use the same resolution function as the ×�Ò ¬ analysis [70]. Good agreement is found between the parameters obtained from the fit region and side-band data. In order to justify using an average of all species, we fit the side-band data with separate parameters for each species. We find no difference between the �� and Ã� parameters, and only small differences in the ÃÃ sample. These differences are probably due to the significant correlation between Gaussian parameters and the fewer number of ÃÃ events in the sample (cf. Table 7-5). The average fit results (Table 7-7) are used in the �È fit. Finally, we check the dependence on tagging by fitting the sub-samples corresponding to each category, as well as the untagged and all-tagged events. We find reasonable consistency across tagging categories, and between tagged and untagged events. The average background parameterization is used for all categories, and systematic errors are evaluated using the other parameterizations. MARCELLA BONA
7.5 Background characterization 175 Figure 7-3. Top: Distributions of ¡Ø in Run 1 (left) and Run 2 (right) on-resonance side-band samples. The result of a triple-Gaussian fit is overlayed. Bottom: The quantity Æ � �� Ô Æ� for each ¡Ø bin, where �� is the value of the function at the center of bin �. Candidates per 0.2ps 1000 500 0 -10 -5 0 5 10 Δt ps 10 3 10 2 10 1 -10 -5 0 5 10 Figure 7-4. The Run 1 � � on-resonance side-band sample compared to a parameterization obtained in the fit region. The parameterization is normalized to the number of events in the side-band sample. Candidates per 0.2ps ANALYSIS OF THE TIME-DEPENDENT �È -VIOLATING ASYMMETRY IN � � � � DECAYS Δt ps
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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
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 and 178: 7.4 Data samples and event selectio
Page 179: 7.5 Background characterization 173
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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