Violation in Mixing

More documents

Recommendations

Info

158 Measurement of Branching Fractions for � � Ã Ë Ã Ë Decays Table 6-5. Upper limits from a cut based analysis estimated with different � Ó× � Ë� cuts and with the Fisher cut giving the best upper limit. � Ó× �Ë� Fisher � of expected bkg events upper limit eff. upper limit on cut cut in the signal box on signal yield � � 0.9 -0.3 1.7 3.9 17.5 8.9 0.8 -0.3 1.7 3.9 17.5 8.9 0.7 -0.2 3.0 4.4 20.5 8.6 0.6 -0.3 1.4 3.4 16.2 8.4 Table 6-6. Upper limits from a cut based analysis estimated with different � Ó× � Ë� cuts, with the Fisher cut giving the best upper limit and the reduced signal box. � Ó× �Ë� Fisher � of expected bkg events upper limit eff. upper limit on cut cut in the signal box on signal yield � � 0.9 -0.3 1.0 3.4 17.0 8.0 0.8 -0.3 1.0 3.4 17.0 8.0 0.7 -0.5 0.0 2.4 9.4 10.2 0.6 -0.5 0.0 2.4 9.3 10.3 We estimate the number of background events in the signal box counting the number of events in the Ñ�Ë side-band region and scaling it by the ratio area(signal box)/area(Ñ�Ë side-band), where the area is the integral of the ARGUS function. We assume that no signal events are observed in the signal box (i.e. no exceeding events are counted in that region). We estimate the ARGUS shape from events having � Ó× �Ë� � �� to have a common parameterization for all the sets of cuts: from the fit in this sample, we get � � � ¦ �� . With this � value, the ratio area(signal box)/area(Ñ�Ë side-band) is � . Some results from this exercise are shown in Tab. 6-5: we quote the results with different cuts on � Ó× �Ë� and with the Fisher cut giving the best upper limit. The yield upper limit is evaluated from the Feldman- Cousins tables [64] assuming that the number of events found is always equal to the estimated number of background events. The results obtained reducing the signal box to �¡�� Å�Î are summarized in Tab. 6-6. The best upper limit on the Ã Ã branching ratio is about � ¡ � . MARCELLA BONA
6.5 Analysis choice 159 6.5 Analysis choice From Monte Carlo and on-resonance data tests, performed in both the maximum likelihood fit case and the counting analysis case, we established that the lowest upper limit can be achieved using the maximum likelihood fit method. We choose to perform this maximum likelihood analysis with the � Ó× �Ë� cut set at the value of ��: this is chosen since, in this case, we have reduced uncertainty on the background distributions. 6.6 Results on the Run1 data-set The final fit sample contains 286 candidates and we find: Æ×�� Æ�� ¦ � The statistical significance of Æ×�� is ��, obtained by fixing the signal component to zero and recording the change in ÐÓ� Ä. In order to test the goodness of fit we have generated a set of pseudo-experiments with Æ×�� and Æ�� : in Fig. 6-9 we plot ÐÓ� Ä for each pseudo-experiment. The arrow indicates the value obtained from the fit to the Run1 data-set. From the simulation we estimate the probability to find a greater value for ÐÓ� Ä to be . 100 80 60 40 20 0 Entries 1000 -5750 -5500 -5250 -5000 -4750 -4500 -4250 -4000 -3750 -3500 Figure 6-9. The value of ÐÓ� Ä for likelihood value pseudo-experiments made by Toy MC. The arrow indicates the result from the fit on the Run1 dataset. MEASUREMENT OF BRANCHING FRACTIONS FOR � � Ã Ë Ã Ë DECAYS
Page 1 and 2:
ÍÒ�Ú�Ö×�Ø��
Page 3 and 4:
Contents Introduction . ...........
Page 5 and 6:
4 Strategy and Tools for Charmless
Page 7 and 8:
7.2 Branching fraction � � �
Page 9 and 10:
Introduction The main goal of the B
Page 11 and 12:
1 �È Violation in the �� Sys
Page 13 and 14:
1.2 Neutral � Mesons 7 Note, howe
Page 15 and 16:
1.2 Neutral � Mesons 9 Applying t
Page 17 and 18:
1.2 Neutral � Mesons 11 �È �
Page 19 and 20:
1.2 Neutral � Mesons 13 ��
Page 21 and 22:
1.2 Neutral � Mesons 15 � ¡�
Page 23 and 24:
1.2 Neutral � Mesons 17 and � a
Page 25 and 26:
1.2 Neutral � Mesons 19 given tim
Page 27 and 28:
1.3 The Three Types of �È Violat
Page 29 and 30:
Page 31 and 32:
Page 33 and 34:
1.4 �È Violation in the Standard
Page 35 and 36:
Page 37 and 38:
Page 39 and 40:
Page 41 and 42:
Page 43 and 44:
1.5 Determination of « 37 1.5 Dete
Page 45 and 46:
1.5 Determination of « 39 Assuming
Page 47 and 48:
1.5 Determination of « 41 Figure 1
Page 49 and 50:
1.5 Determination of « 43 b q (a)
Page 51 and 52:
1.5 Determination of « 45 � �
Page 53 and 54:
2 The BABAR Experiment 2.1 Physics
Page 55 and 56:
2.1 Physics at � � B Factory op
Page 57 and 58:
2.2 The BABAR detector. 51 Integrat
Page 59 and 60:
2.2 The BABAR detector. 53 The dete
Page 61 and 62:
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 93 and 94:
3.3 Studies on data 87 0.504 0.502
Page 95 and 96:
3.3 Studies on data 89 N(π + π -
Page 97 and 98:
3.3 Studies on data 91 0.508 0.506
Page 99 and 100:
3.3 Studies on data 93 Figure 3-13.
Page 101 and 102:
3.3 Studies on data 95 0.508 0.506
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 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: 6.4 Counting analysis 157 120 100 8
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
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.
show all

Violation in Mixing

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?