Standard Model Fit results: Summer 2021

Parameter Input value Full fit SM Prediction Pull
\bar{\rho} - - 0.158 \pm 0.010 -
\bar{\eta} - - 0.3496 \pm 0.0094 -
\rho - - 0.162 \pm 0.010 -
\eta - - 0.3589 \pm 0.0097 -
A - - 0.838 \pm 0.012 -
\lambda 0.2276 \pm 0.0012 - 0.22489 \pm 0.00070 -2.0
\sin\theta_{12} - - 0.22489 \pm 0.00070 -
\sin\theta_{23} - - 0.04252 \pm 0.00056 -
\sin\theta_{13} - - 0.003771 \pm 0.000094 -
\delta [^{\circ}] - - 65.6 \pm 1.4 -
|V_{ub}| - 0.003770 \pm 0.000093 - -
|V_{ub}| - - - -
|V_{ub}| - - - -
|V_{cb}| - 0.04252 \pm 0.00056 - -
|V_{cb}| - - - -
|V_{cb}| - - - -
\alpha [^{\circ}] 85.5 \pm 4.5 91.7 \pm 1.5 92.6 \pm 1.6 +1.4
\beta [^{\circ}] - 22.57 \pm 0.60 24.2 \pm 1.3 -
\gamma [^{\circ}] 65.7 \pm 3.4 65.5 \pm 1.4 - -
J_{cp}\cdot10^{5} - - 3.201 \pm 0.099 -
2\beta+\gamma [^{\circ}] -90 \pm 56 \text{ and } 94 \pm 52 110.8 \pm 1.8 110.9 \pm 1.8 +0.3
\sin(2\beta) 0.698 \pm 0.017 0.708 \pm 0.014 0.747 \pm 0.031 +1.4
\cos(2\beta) 0.88 \pm 0.11 0.704 \pm 0.014 0.661 \pm 0.035 -1.9
\beta_{s} [^{\circ}] - - 1.059 \pm 0.029 -
m_{c}\mathrm{ [GeV/c^{2}]} - - - -
m_{b}\mathrm{ [GeV/c^{2}]} - - - -
m_{t}\mathrm{ [GeV/c^{2}]} - 163.25 \pm 0.45 - -
B_{k} - 0.720 \pm 0.019 - -
f_{B_{s}} - 0.2301 \pm 0.0014 - -
f_{B_{s}}/f_{B_{d}} - 1.2090 \pm 0.0040 - -
B_{B_{s}}/B_{B_{d}} - 1.025 \pm 0.021 - -
B_{B_{s}} - 0.820 \pm 0.021 - -
B_{k} - 0.720 \pm 0.019 - -
|\epsilon_{k}|\cdot 10^{3} 2.228 \pm 0.011 2.227 \pm 0.011 - -
\Delta m_{s} \mathrm{[ps^{-1}]} - 17.7600 \pm 0.0100 16.78 \pm 0.68 -
\Delta m_{d} \mathrm{[ps^{-1}]} - - - -
\Delta\Gamma_{d}/\Gamma_{d} - - 0.00530 \pm 0.00065 -
\Delta\Gamma_{s}/\Gamma_{s} - - 0.165 \pm 0.020 -
A_{SL_{d}} - -0.000376 \pm 0.000093 -0.000376 \pm 0.000093 -
A_{SL_{s}} - 0.0000163 \pm 0.0000040 0.0000163 \pm 0.0000040 -
B(B\rightarrow\tau\nu)\cdot 10^{4} 1.09 \pm 0.24 0.909 \pm 0.046 0.903 \pm 0.047 -0.8
\bar {B}(B_{s}\rightarrow ll)\cdot10^{9} - 4.02 \pm 0.12 4.06 \pm 0.13 -
B(B_{d}\rightarrow ll)\cdot10^{9} - 0.1085 \pm 0.0035 0.1092 \pm 0.0036 -

CKM matrix thus looks like V_{CKM}=\left(\begin{array}{ccc} (0.97435 \pm 0.00014) & (0.22501 \pm 0.00064) & (-)e^{i(-65.5 \pm 1.3)^\circ}\\ ( -0.22492 \pm 0.00064)e^{i(0.0362 \pm 0.0011)^\circ} & (0.97345 \pm 0.00014)e^{i(-0.001933 \pm 0.000055)^\circ} & (-) \\ (0.00872 \pm 0.00013)e^{i(-22.53 \pm 0.60)^\circ} & ( -0.04178 \pm 0.00055)e^{i(1.058 \pm 0.028)^\circ} & (0.999088 \pm 0.000024)\end{array}\right)




Full fit result for \,\bar{\rho}
0.158 \pm 0.010
95% prob:[0.1390, 0.1787]
99% prob:[0.1292, 0.1887]
EPS - PDF - PNG - JPG - GIF



Full fit result for \,\bar{\eta}
0.3496 \pm 0.0094
95% prob:[0.3311, 0.3688]
99% prob:[0.3221, 0.3788]
EPS - PDF - PNG - JPG - GIF




Full fit result for \,\bar{\rho} - \bar{\eta}



EPS - PDF - PNG - JPG - GIF



Angles only result for \,\bar{\rho} - \bar{\eta}



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Sides only result for \,\bar{\rho} - \bar{\eta}



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Full fit result for \,\rho
0.162 \pm 0.010
95% prob:[0.142, 0.183]
99% prob:[0.132, 0.193]
EPS - PDF - PNG - JPG - GIF



Full fit result for \,\eta
0.3589 \pm 0.0097
95% prob:[0.3397, 0.3784]
99% prob:[0.3305, 0.3886]
EPS - PDF - PNG - JPG - GIF




Full fit result for \,A
0.838 \pm 0.012
95% prob:[0.815, 0.864]
99% prob:[0.803, 0.877]
EPS - PDF - PNG - JPG - GIF




Fit Input for \,\lambda
0.2276 \pm 0.0012
95% prob:[0.2253, 0.2299]
99% prob:[0.2239, 0.2300]
EPS - PDF - PNG - JPG - GIF



Prediction for \,\lambda
0.22489 \pm 0.00070
95% prob:[0.22366, 0.22633]
99% prob:[0.22297, 0.22703]
EPS - PDF - PNG - JPG - GIF




Full fit result for \,\sin\theta_{12}
0.22489 \pm 0.00070
95% prob:[0.22366, 0.22633]
99% prob:[0.22297, 0.22703]
EPS - PDF - PNG - JPG - GIF




Full fit result for \,\sin\theta_{23}
0.04252 \pm 0.00056
95% prob:[0.04137, 0.04365]
99% prob:[0.04080, 0.04423]
EPS - PDF - PNG - JPG - GIF




Full fit result for \,\sin\theta_{13}
0.003771 \pm 0.000094
95% prob:[0.003583, 0.003959]
99% prob:[0.003497, 0.004062]
EPS - PDF - PNG - JPG - GIF




Full fit result for \,\delta [^{\circ}]
65.6 \pm 1.4
95% prob:[62.7, 68.5]
99% prob:[61.3, 70.0]
EPS - PDF - PNG - JPG - GIF




Fit Input for \,|V_{ub}|
Gaussian likelihood used
-

EPS - PDF - PNG - JPG - GIF



Full Fit result for \,|V_{ub}|
0.003770 \pm 0.000093
95% prob:[0.003583, 0.003959]
99% prob:[0.003494, 0.004060]
EPS - PDF - PNG - JPG - GIF



SM Fit prediction for \,|V_{ub}|
-
95% prob:0
99% prob:0
EPS - PDF - PNG - JPG - GIF
./makepull springPDG22_novub Vubfit1 fit1 -val=0.0 -err=0.0 --makeps -range=``[0.0,0.0]x[0.0,0.0]'' -season=springPDG22 -bins=``[150]x[100]'' -xlab=\|V_{ub}\| >& tmp2.log


Compatibility Plot for \,|V_{ub}|
{\rm pull}(|V_{ub}|) = -

EPS - PDF - PNG - JPG - GIF




Fit Input for \,|V_{ub}|
Gaussian likelihood used
-

EPS - PDF - PNG - JPG - GIF



Full Fit result for \,|V_{ub}|
-
95% prob:0
99% prob:0
EPS - PDF - PNG - JPG - GIF



SM Fit prediction for \,|V_{ub}|
-
95% prob:0
99% prob:0
EPS - PDF - PNG - JPG - GIF
./makepull springPDG22_sm Vub_exclfit1 fit1 -val=0.0 -err=0.0 --makeps -range=``[0.0,0.0]x[0.0,0.0]'' -season=springPDG22 -bins=``[150]x[100]'' -xlab=\|V_{ub}\| >& tmp2.log


Compatibility Plot for \,|V_{ub}|
{\rm pull}(|V_{ub}|) = -

EPS - PDF - PNG - JPG - GIF




Fit Input for \,|V_{ub}|
Gaussian likelihood used
-

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Full Fit result for \,|V_{ub}|
-
95% prob:0
99% prob:0
EPS - PDF - PNG - JPG - GIF



SM Fit prediction for \,|V_{ub}|
-
95% prob:0
99% prob:0
EPS - PDF - PNG - JPG - GIF
./makepull springPDG22_sm Vub_inclfit1 fit1 -val=0.0 -err=0.0 --makeps -range=``[0.0,0.0]x[0.0,0.0]'' -season=springPDG22 -bins=``[150]x[100]'' -xlab=\|V_{ub}\| >& tmp2.log


Compatibility Plot for \,|V_{ub}|
{\rm pull}(|V_{ub}|) = -

EPS - PDF - PNG - JPG - GIF




Fit Input for \,|V_{cb}|
Gaussian likelihood used
-

EPS - PDF - PNG - JPG - GIF



Full Fit result for \,|V_{cb}|
0.04252 \pm 0.00056
95% prob:[0.04137, 0.04365]
99% prob:[0.04080, 0.04423]
EPS - PDF - PNG - JPG - GIF



SM Fit prediction for \,|V_{cb}|
-
95% prob:0
99% prob:0
EPS - PDF - PNG - JPG - GIF
./makepull springPDG22_novcb Vcbfit1 fit1 -val=0.0 -err=0.0 --makeps -range=``[0.0,0.0]x[0.0,0.0]'' -season=springPDG22 -bins=``[150]x[100]'' -xlab=\|V_{cb}\| >& tmp2.log


Compatibility Plot for \,|V_{cb}|
{\rm pull}(|V_{cb}|) = -

EPS - PDF - PNG - JPG - GIF




Fit Input for \,|V_{cb}|
Gaussian likelihood used
-

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Full Fit result for \,|V_{cb}|
-
95% prob:0
99% prob:0
EPS - PDF - PNG - JPG - GIF



SM Fit prediction for \,|V_{cb}|
-
95% prob:0
99% prob:0
EPS - PDF - PNG - JPG - GIF
./makepull springPDG22_sm Vcb_exclfit1 fit1 -val=0.0 -err=0.0 --makeps -range=``[0.0,0.0]x[0.0,0.0]'' -season=springPDG22 -bins=``[150]x[100]'' -xlab=\|V_{cb}\| >& tmp2.log


Compatibility Plot for \,|V_{cb}|
{\rm pull}(|V_{cb}|) = -

EPS - PDF - PNG - JPG - GIF




Fit Input for \,|V_{cb}|
Gaussian likelihood used
-

EPS - PDF - PNG - JPG - GIF



Full Fit result for \,|V_{cb}|
-
95% prob:0
99% prob:0
EPS - PDF - PNG - JPG - GIF



SM Fit prediction for \,|V_{cb}|
-
95% prob:0
99% prob:0
EPS - PDF - PNG - JPG - GIF
./makepull springPDG22_sm Vcb_inclfit1 fit1 -val=0.0 -err=0.0 --makeps -range=``[0.0,0.0]x[0.0,0.0]'' -season=springPDG22 -bins=``[150]x[100]'' -xlab=\|V_{cb}\| >& tmp2.log


Compatibility Plot for \,|V_{cb}|
{\rm pull}(|V_{cb}|) = -

EPS - PDF - PNG - JPG - GIF




Fit Input for \,\alpha [^{\circ}]
85.5 \pm 4.5
95% prob:[76.2, 94.7]
99% prob:[71.7, 99.2]
EPS - PDF - PNG - JPG - GIF



Full Fit result for \,\alpha [^{\circ}]
91.7 \pm 1.5
95% prob:[88.7, 94.9]
99% prob:[87.2, 96.4]
EPS - PDF - PNG - JPG - GIF



SM Fit prediction for \,\alpha [^{\circ}]
92.6 \pm 1.6
95% prob:[89.2, 95.9]
99% prob:[87.7, 97.5]
EPS - PDF - PNG - JPG - GIF
./makepull springPDG22_sm alphafit2 fit2 -val=85.5 -err=4.6 --makeps -range=``[77.80000000000001,107.5]x[0.0,9.2]'' -season=springPDG22 -bins=``[150]x[100]'' -xlab=#alpha [^{#circ}] >& tmp2.log


Compatibility Plot for \,\alpha [^{\circ}]
{\rm pull}(\alpha ) = +1.4

EPS - PDF - PNG - JPG - GIF




Full Fit result for \,\beta [^{\circ}]
22.57 \pm 0.60
95% prob:[21.37, 23.80]
99% prob:[20.80, 24.46]
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SM Fit prediction for \,\beta [^{\circ}]
24.2 \pm 1.3
95% prob:[21.6, 26.9]
99% prob:[20.3, 28.0] U [28.1, 28.4]
EPS - PDF - PNG - JPG - GIF




Fit Input for \,\gamma [^{\circ}]
65.7 \pm 3.4
95% prob:[59.0, 72.7]
99% prob:[55.5, 75.7]
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Full Fit result for \,\gamma [^{\circ}]
65.5 \pm 1.4
95% prob:[62.7, 68.4]
99% prob:[61.2, 69.9]
EPS - PDF - PNG - JPG - GIF



SM Fit prediction for \,\gamma [^{\circ}]
-
95% prob:0
99% prob:0
EPS - PDF - PNG - JPG - GIF
./makepull springPDG22_nogamma gammafit1 fit1 -val=65.75 -err=3.45 --makeps -range=``[-6.575,72.325]x[0.0,6.9]'' -season=springPDG22 -bins=``[150]x[100]'' -xlab=#gamma [^{#circ}] >& tmp2.log


Compatibility Plot for \,\gamma [^{\circ}]
{\rm pull}(\gamma ) = -

EPS - PDF - PNG - JPG - GIF




Full fit result for \,J_{cp}\cdot10^{5}
3.201 \pm 0.099
95% prob:[3.007, 3.399]
99% prob:[2.924, 3.488]
EPS - PDF - PNG - JPG - GIF




Fit Input for \,2\beta+\gamma [^{\circ}]
-90 \pm 56 \text{ and } 94 \pm 52
95% prob:[-166, 166]
99% prob:[-179, 179]
EPS - PDF - PNG - JPG - GIF



Full Fit result for \,2\beta+\gamma [^{\circ}]
110.8 \pm 1.8
95% prob:[107.0, 114.4]
99% prob:[105.2, 116.2]
EPS - PDF - PNG - JPG - GIF



SM Fit prediction for \,2\beta+\gamma [^{\circ}]
110.9 \pm 1.8
95% prob:[107.2, 114.5]
99% prob:[105.3, 116.3]
EPS - PDF - PNG - JPG - GIF
./makepull springPDG22_sm 2bpgfit4 fit4 -val=94 -err=56.4 --makeps -range=``[-110.09,130.99]x[0.0,112.8]'' -season=springPDG22 -bins=``[150]x[100]'' -xlab=2#beta+#gamma [^{#circ}] >& tmp2.log


Compatibility Plot for \,2\beta+\gamma [^{\circ}]
{\rm pull}(2\beta+\gamma ) = +0.3

EPS - PDF - PNG - JPG - GIF




Fit Input for \,\sin(2\beta)
0.698 \pm 0.017
95% prob:[0.665, 0.733]
99% prob:[0.649, 0.751]
EPS - PDF - PNG - JPG - GIF



Full Fit result for \,\sin(2\beta)
0.708 \pm 0.014
95% prob:[0.679, 0.739]
99% prob:[0.664, 0.754]
EPS - PDF - PNG - JPG - GIF



SM Fit prediction for \,\sin(2\beta)
0.747 \pm 0.031
95% prob:[0.687, 0.810]
99% prob:[0.655, 0.838]
EPS - PDF - PNG - JPG - GIF
./makepull springPDG22_sm sin2bfit3 fit3 -val=0.699 -err=0.017 --makeps -range=``[0.4740000000000001,1.0230000000000001]x[0.0,0.034]'' -season=springPDG22 -bins=``[150]x[100]'' -xlab=sin\(2#beta\) >& tmp2.log


Compatibility Plot for \,\sin(2\beta)
{\rm pull}(sin(2\beta)) = +1.4

EPS - PDF - PNG - JPG - GIF




Fit Input for \,\cos(2\beta)
0.88 \pm 0.11
95% prob:[0.54, 0.99]
99% prob:[0.31, 0.99]
EPS - PDF - PNG - JPG - GIF



Full Fit result for \,\cos(2\beta)
0.704 \pm 0.014
95% prob:[0.674, 0.734]
99% prob:[0.657, 0.748]
EPS - PDF - PNG - JPG - GIF



SM Fit prediction for \,\cos(2\beta)
0.661 \pm 0.035
95% prob:[0.590, 0.729]
99% prob:[0.545, 0.553] U [0.558, 0.759]
EPS - PDF - PNG - JPG - GIF
./makepull springPDG22_sm cos2bfit3 fit3 -val=0.883 -err=0.113 --makeps -range=``[0.35064940000000006,0.9786406000000001]x[0.0,0.226]'' -season=springPDG22 -bins=``[150]x[100]'' -xlab=cos\(2#beta\) >& tmp2.log


Compatibility Plot for \,\cos(2\beta)
{\rm pull}(cos(2\beta)) = -1.9

EPS - PDF - PNG - JPG - GIF




Fit Input for \,\beta_{s} [^{\circ}]
-
95% prob:0
99% prob:0
EPS - PDF - PNG - JPG - GIF



Prediction for \,\beta_{s} [^{\circ}]
1.059 \pm 0.029
95% prob:[1.002, 1.117]
99% prob:[0.976, 1.147]
EPS - PDF - PNG - JPG - GIF




Fit Input for \,m_{t}\mathrm{ [GeV/c^{2}]}
Gaussian likelihood used
-

EPS - PDF - PNG - JPG - GIF



Full Fit result for \,m_{t}\mathrm{ [GeV/c^{2}]}
163.25 \pm 0.45
95% prob:[162.60, 164.30]
99% prob:[162.19, 164.70]
EPS - PDF - PNG - JPG - GIF



SM Fit prediction for \,m_{t}\mathrm{ [GeV/c^{2}]}
-
95% prob:0
99% prob:0
EPS - PDF - PNG - JPG - GIF
./makepull springPDG22_nomt mTopfit1 fit1 -val=0.0 -err=0.0 --makeps -range=``[0.0,0.0]x[0.0,0.0]'' -season=springPDG22 -bins=``[150]x[100]'' -xlab=m_{t} [GeV/c^{2}] >& tmp2.log


Compatibility Plot for \,m_{t}\mathrm{ [GeV/c^{2}]}
{\rm pull}(m_{t} ) = -

EPS - PDF - PNG - JPG - GIF




Fit Input for \,B_{k}
-
95% prob:[0.682, 0.760]
99% prob:[0.662, 0.779]
EPS - PDF - PNG - JPG - GIF



Full Fit result for \,B_{k}
0.720 \pm 0.019
95% prob:[0.684, 0.761]
99% prob:[0.665, 0.779]
EPS - PDF - PNG - JPG - GIF



SM Fit prediction for \,B_{k}
-
95% prob:0
99% prob:0
EPS - PDF - PNG - JPG - GIF
./makepull springPDG22_nobk Bkfit1 fit1 -val=0.0 -err=0.0 --makeps -range=``[0.0,0.0]x[0.0,0.0]'' -season=springPDG22 -bins=``[150]x[100]'' -xlab=B_{k} >& tmp2.log


Compatibility Plot for \,B_{k}
{\rm pull}(B_{k}) = -

EPS - PDF - PNG - JPG - GIF




Fit Input for \,f_{B_{s}}
Gaussian likelihood used
-

EPS - PDF - PNG - JPG - GIF
 
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