Violation in Mixing

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108 Strategy and Tools for Charmless Two-body � Decays Analysis Cut �¡Å � �� Î� � � �¡Å �Å Ã� Ñ � � � � � � Ô £ � � ��Î� Ó× � £ Ã � �� Ô� �� Î� or �� ÔÃ �� Î� Table 4-3. Cuts used to select a control sample of � £ � Ã � � decays. candidate). Candidates in � mass side-band regions are used for background subtraction. The resulting sample is used to parameterize the � distributions for pions and kaons. A simple set of cuts were used to select a very clean sample of � £ decays in which either the Ã or � track from the � decay has a momentum in the range �� Î� , covering � of the momentum range of the daughters of the charmless two-body decays. The cuts are summarized in Table 4-3. The loose goodtrack 2 definition is used for selecting � daughter tracks and a very-loose good-track 3 definition is used for the slow pion from the � £ decay. ¡Å is the measured mass difference between the � £ candidate and the � candidate, �¡Å is the measured resolution on this quantity, Å Ã� is the reconstructed � mass and �� is the resolution on Å Ã� . The mass resolutions are measured to be ��Å�Î� for �¡Å and �Å�Î� for �� . The quantity Ó× � £ Ã is the cosine of the angle of the kaon track with respect to the � flight direction, measured in the � center-of-mass system. For signal decays, this distribution is flat, whereas the combinatorial background is peaked in the forward and backward directions. The same set of cuts is used to select a sample of � £ in the Monte Carlo simulated data. The � parameterizations obtained from this sample are used in constructing the PDFs for fits to Monte Carlo events. This sample is also used to check that the Monte Carlo accurately simulates the efficiency of the PID cuts. This is demonstrated in Figs. 4-6 which display the efficiencies of the PID � � cut used in the � � � � analysis for kaons and pions, respectively, for both the data and Monte Carlo control samples. There is good agreement between the two samples. Figs. 4-7 compare the efficiencies obtained from the Monte Carlo � £ control sample to those obtained directly from Monte Carlo � � � � decays. Good agreement is observed between the efficiency of the PID cuts in Monte Carlo simulated events and that obtained from this control sample. Thus, the PID efficiencies obtained from Monte Carlo signal events are used without any corrections. 2 see Sec. 4.2.2 3 The very loose good track selection does not include the ÔÌ and Æ ��Àhits cuts with respect to the loose good track one in Sec. 4.2.2. MARCELLA BONA
4.4 PID selection 109 θ c Cut Efficiency 1.1 1 0.9 Data (D * ) MC (D * ) B A B AR θ c Cut Efficiency Data (D * ) MC (D * ) B A B AR 0.8 0.8 2 2.5 3 3.5 4 2 2.5 3 3.5 4 Figure 4-6. Kaon Momentum (GeV/c) Left plot: the efficiency versus momentum of the � � Pion Momentum (GeV/c) cut for kaons in the � £ control sample in data (filled circles) and Monte Carlo simulation (open diamonds). Right plot: the efficiency versus momentum of the � � cut for pions in the � £ control sample in data (filled circles) and Monte Carlo simulation (open diamonds) θ c Cut Efficiency 1.1 1 0.9 MC (D * ) MC (B 0 Kπ) 0.8 0.8 2 2.5 3 3.5 4 2 2.5 3 3.5 4 Figure 4-7. Kaon Momentum (GeV/c) Left plot: the efficiency versus momentum of the � � Pion Momentum (GeV/c) cut for kaons in the Monte Carlo � £ control sample (filled circles) and Monte Carlo simulated � � � � decays (open diamonds). Right plot: the efficiency versus momentum of the � � cut for pions in the Monte Carlo � £ control sample (filled circles) and Monte Carlo simulated � � � � decays (open diamonds). 4.4.2 Selector-based PID θ c Cut Efficiency 1.1 1 0.9 1.1 1 0.9 MC (D * ) MC (B 0 Kπ) The selector method of particle ID attempts to identify kaons and pions on a per-track basis by cutting on a likelihood function derived using information from the ËÎÌ, ��À and �ÁÊ� subdetectors. The standard BABAR selector is called KaonSMSSelector (hereafter referred to as SMS) and provides decisions based on the comparison of the likelihoods for different mass hypotheses: Ã, �, and proton. Each likelihood is composed of products of individual subdetector likelihoods for the given hypothesis: Ä � �Ä ËÎÌ � £ Ä ��À � £ Ä �ÁÊ� � � where � � �� Ã� Ô 4 . The ËÎÌ and ��À likelihoods are calculated assuming Gaussian ��Ü distributions while the �ÁÊ� likelihood is the product of the � ��Ö�Ò�ÓÚ angle Gaussian likelihood and the Poissonian likelihood for the number of � ��Ö�Ò�ÓÚ photons measured compared to expected for each hypothesis. In addition, the �ÁÊ� is used in veto mode for particles below the � ��Ö�Ò�ÓÚ threshold for kaons. The SMS selector provides several levels of purity: 4 Ô stands for proton. STRATEGY AND TOOLS FOR CHARMLESS TWO-BODY � DECAYS ANALYSIS
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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
Page 63 and 64: 2.2 The BABAR detector. 57 SVT Effi
Page 65 and 66: 2.2 The BABAR detector. 59 324 1015
Page 67 and 68: Efficiency 2.2 The BABAR detector.
Page 69 and 70: 2.2 The BABAR detector. 63 2.2.4 Th
Page 71 and 72: entries per mrad 2.2 The BABAR dete
Page 73 and 74: 2.2 The BABAR detector. 67 entries
Page 75 and 76: 2.2 The BABAR detector. 69 energies
Page 77 and 78: 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: 4.4 PID selection 107 In the case o
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 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
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6.5 Analysis choice 159 6.5 Analysi
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6.6 Results on the Run1 data-set 16
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6.7 Determination of the branching
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7 Analysis of the time-dependent
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7.3 Analysis strategy 167 Parameter
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7.4 Data samples and event selectio
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7.4 Data samples and event selectio
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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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