Difference: ResultsWinter2016NP (1 vs. 3)

Revision 3
11 Jan 2016 - Main.DenisDerkach
Line: 1 to 1
 
META TOPICPARENT name="ResultsWinter2016"

New Physics Fit results: Winter 2016

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Input used are the same as in Standard Model Fit.
  The fit presented here is meant to constrain the NP contributions to |Δ F|=2 transitions by using the available experimental information on loop-mediated processes In general, NP models introduce a large number of new parameters: flavour changing couplings, short distance coefficients and matrix elements of new local operators. The specific list and the actual values of these parameters can only be determined within a given model. Nevertheless mixing processes are described by a single amplitude and can be parameterized, without loss of generality, in terms of two parameters, which quantify the difference of the complex amplitude with respect to the SM one. Thus, for instance, in the case of B^0_q-\bar{B}^0_q mixing we define
C_{B_q}  \, e^{2 i \phi_{B_q}} = \frac{\langle B^0_q|H_\mathrm{eff}^\mathrm{full}|\bar{B}^0_q\rangle} {\langle
              B^0_q|H_\mathrm{eff}^\mathrm{SM}|\bar{B}^0_q\rangle}\,, \qquad (q=d,s),
where H_\mathrm{eff}^\mathrm{SM} includes only the SM box diagrams, while H_\mathrm{eff}^\mathrm{full} also includes the NP contributions. In the absence of NP effects, C_{B_q}=1 and \phi_{B_q}=0 by definition. In a similar way, one can write
C_{\epsilon_K} = \frac{\mathrm{Im}[\langle
Revision 2
09 Jan 2016 - Main.DenisDerkach
Line: 1 to 1
Changed:
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META TOPICPARENT name="ResultsSummer2014PostMoriond"

New Physics Fit results: Summer 2015

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META TOPICPARENT name="ResultsWinter2016"

New Physics Fit results: Winter 2016

 

The fit presented here is meant to constrain the NP contributions to |Δ F|=2 transitions by using the available experimental information on loop-mediated processes In general, NP models introduce a large number of new parameters: flavour changing couplings, short distance coefficients and matrix elements of new local operators. The specific list and the actual values of these parameters can only be determined within a given model. Nevertheless mixing processes are described by a single amplitude and can be parameterized, without loss of generality, in terms of two parameters, which quantify the difference of the complex amplitude with respect to the SM one. Thus, for instance, in the case of B^0_q-\bar{B}^0_q mixing we define
C_{B_q}  \, e^{2 i \phi_{B_q}} = \frac{\langle B^0_q|H_\mathrm{eff}^\mathrm{full}|\bar{B}^0_q\rangle} {\langle
              B^0_q|H_\mathrm{eff}^\mathrm{SM}|\bar{B}^0_q\rangle}\,, \qquad (q=d,s),
where H_\mathrm{eff}^\mathrm{SM} includes only the SM box diagrams, while H_\mathrm{eff}^\mathrm{full} also includes the NP contributions. In the absence of NP effects, C_{B_q}=1 and \phi_{B_q}=0 by definition. In a similar way, one can write
Line: 32 to 32
  All the measured observables can be written as a function of these NP parameters and the SM ones ρ and η, and additional parameters such as masses, form factors, and decay constants.

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Parameter Input value Full fit
\bar{\rho} - 0.147 \pm 0.043
\bar{\eta} - 0.384 \pm 0.044
\rho - 0.151 \pm 0.044
\eta - 0.394 \pm 0.045
A - 0.791 \pm 0.02
\lambda 0.22519 \pm 0.00087 0.225 \pm 0.00055
C_{B_{d}} - 1.09 \pm 0.15
\phi_{B_{d}} [^{\circ}] - -2.9 \pm 2.8
C_{B_{s}} - 1.141 \pm 0.087
\phi_{B_{s}} [^{\circ}] - -0.026 \pm 0.959
C_{\epsilon_{K}} - 1.07 \pm 0.15
A_{SL_{d}} -0.0015 \pm 0.0017 -0.0028 \pm 0.0015
A_{SL_{s}} -0.0075 \pm 0.0041 -0.00032 \pm 0.00052

The fit results for all the nine CKM elements are V_{CKM}=\left(\begin{array}{ccc} (0.97432 \pm 0.00015) & (0.22506 \pm 0.00061) & (0.00385 \pm 0.00039)e^{i(-69.0 \pm 5.7)^\circ}\\ ( -0.22493 \pm 0.00066)e^{i(0.0353 \pm 0.0039)^\circ} & (0.97352 \pm 0.00015)e^{i(-0.00188 \pm 0.0002)^\circ} & (0.0401 \pm 0.001) \\ (0.00845 \pm 0.00044)e^{i(-24.3 \pm 2.3)^\circ} & ( -0.03939 \pm 0.00099)e^{i(1.13 \pm 0.11)^\circ} & (0.999181 \pm 4.295\times 10^{-5})\end{array}\right)




Full fit result for \,\bar{\rho}
0.147 \pm 0.043
95% prob:[0.067, 0.228]
99% prob:[0.040, 0.269]
EPS - PDF - PNG - JPG - GIF




Full fit result for \,\bar{\eta}
0.384 \pm 0.044
95% prob:[0.297, 0.472]
99% prob:[0.258, 0.517]
EPS - PDF - PNG - JPG - GIF



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



EPS - PDF - PNG - JPG - GIF




Full fit result for \,\rho
0.151 \pm 0.044
95% prob:[0.069, 0.234]
99% prob:[0.040, 0.275]
EPS - PDF - PNG - JPG - GIF



Full fit result for \,\eta
0.394 \pm 0.045
95% prob:[0.305, 0.484]
99% prob:[0.266, 0.532]
EPS - PDF - PNG - JPG - GIF




Full fit result for \,A
0.791 \pm 0.02
95% prob:[0.749, 0.832]
99% prob:[0.729, 0.853]
EPS - PDF - PNG - JPG - GIF




Fit Input for \,\lambda
0.22519 \pm 0.00087
95% prob:[0.22356, 0.22712]
99% prob:[0.22257, 0.22802]
EPS - PDF - PNG - JPG - GIF



Full Fit result for \,\lambda
0.225 \pm 0.00055
95% prob:[0.22386, 0.22633]
99% prob:[0.22316, 0.22693]
EPS - PDF - PNG - JPG - GIF




Full fit result for \,C_{B_{d}}
1.09 \pm 0.15
95% prob:[0.79, 1.40]
99% prob:[0.69, 1.60]
EPS - PDF - PNG - JPG - GIF



Full fit result for \,\phi_{B_{d}} [^{\circ}]
-2.9 \pm 2.8
95% prob:[-8., 2.7]
99% prob:[-11, 5.4]
EPS - PDF - PNG - JPG - GIF



correlations for \,\Phi_{B_{d}} - C_{B_{d}}



EPS - PDF - PNG - JPG - GIF




Full fit result for \,C_{B_{s}}
1.141 \pm 0.087
95% prob:[0.973, 1.322]
99% prob:[0.904, 1.432]
EPS - PDF - PNG - JPG - GIF



Full fit result for \,\phi_{B_{s}} [^{\circ}]
-0.026 \pm 0.959
95% prob:[-1.935, 1.897]
99% prob:[-2.892, 2.857]
EPS - PDF - PNG - JPG - GIF



correlations for \,\Phi_{B_{s}} - C_{B_{s}}



EPS - PDF - PNG - JPG - GIF




Full fit result for \,C_{\epsilon_{K}}
1.07 \pm 0.15
95% prob:[0.8, 1.37]
99% prob:[0.71, 1.59]
EPS - PDF - PNG - JPG - GIF




Fit Input for \,A_{SL_{d}}
Gaussian likelihood used
-0.0015 \pm 0.0017

EPS - PDF - PNG - JPG - GIF



Full Fit result for \,A_{SL_{d}}
-0.0028 \pm 0.0015
95% prob:[-0.0057, -0.0001]
99% prob:[-0.0069, 0.00104]
EPS - PDF - PNG - JPG - GIF




Fit Input for \,A_{SL_{s}}
Gaussian likelihood used
-0.0075 \pm 0.0041

EPS - PDF - PNG - JPG - GIF



Full Fit result for \,A_{SL_{s}}
-0.00032 \pm 0.00052
95% prob:[-0.0012, 0.00072]
99% prob:[-0.0015, 0.00123]
EPS - PDF - PNG - JPG - GIF

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Parameter Input value Prediction
\bar{\rho} - 0.146 \pm 0.043
\bar{\eta} - 0.384 \pm 0.043
\rho - 0.150 \pm 0.044
\eta - 0.394 \pm 0.044
A - 0.789 \pm 0.021
\lambda 0.22518 \pm 0.00087 0.22500 \pm 0.00054
|V_{ub}| - 0.00364 \pm 0.00012
|V_{cb}| - 0.04237 \pm 0.00062
\alpha [^{\circ}] - 87.4 \pm 6.2
\beta [^{\circ}] - 23.7 \pm 2.6
\gamma [^{\circ}] - 66.9 \pm 3.0
C_{B_{d}} - 1.08 \pm 0.15
\phi_{B_{d}} [^{\circ}] - -2.8 \pm 2.8
C_{B_{s}} - 1.141 \pm 0.087
\phi_{B_{s}} [^{\circ}] - -0.026 \pm 0.959
C_{\epsilon_{K}} - 1.07 \pm 0.14
A_{SL_{d}} -0.0015 \pm 0.0017 -0.0028 \pm 0.0014
A_{SL_{s}} -0.0075 \pm 0.0041 -0.00033 \pm 0.00052

CKM matrix thus looks like V_{CKM}=\left(\begin{array}{ccc} (0.97432 \pm 0.00015) & (0.22505 \pm 0.00061) & (0.00364 \pm 0.00012)e^{i(-69.0 \pm 5.6)^\circ}\\ ( -0.22493 \pm 0.00066)e^{i(0.0353 \pm 0.0039)^\circ} & (0.97352 \pm 0.00014)e^{i(-0.00188 \pm 0.00020)^\circ} & (0.04237 \pm 0.00062) \\ (0.00844 \pm 0.00044)e^{i(-24.1 \pm 2.3)^\circ} & ( -0.0393 \pm 0.0010)e^{i(1.12 \pm 0.10)^\circ} & (0.999183 \pm 0.000039)\end{array}\right)




Full fit result for \,\bar{\rho}
0.146 \pm 0.043
95% prob:[0.067, 0.229]
99% prob:[0.040, 0.271]
EPS - PDF - PNG - JPG - GIF




Full fit result for \,\bar{\eta}
0.384 \pm 0.043
95% prob:[0.297, 0.471]
99% prob:[0.256, 0.517]
EPS - PDF - PNG - JPG - GIF



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



EPS - PDF - PNG - JPG - GIF




Full fit result for \,\rho
0.150 \pm 0.044
95% prob:[0.069, 0.234]
99% prob:[0.041, 0.278]
EPS - PDF - PNG - JPG - GIF



Full fit result for \,\eta
0.394 \pm 0.044
95% prob:[0.305, 0.484]
99% prob:[0.264, 0.531]
EPS - PDF - PNG - JPG - GIF




Full fit result for \,A
0.789 \pm 0.021
95% prob:[0.750, 0.832]
99% prob:[0.729, 0.853]
EPS - PDF - PNG - JPG - GIF




Fit Input for \,\lambda
0.22518 \pm 0.00087
95% prob:[0.22356, 0.22712]
99% prob:[0.22257, 0.22801]
EPS - PDF - PNG - JPG - GIF



Prediction for \,\lambda
0.22500 \pm 0.00054
95% prob:[0.22386, 0.22633]
99% prob:[0.22316, 0.22693]
EPS - PDF - PNG - JPG - GIF




Full fit result for \,|V_{ub}|
0.00364 \pm 0.00012
95% prob:[0.00340, 0.00389]
99% prob:[0.00329, 0.00404]
EPS - PDF - PNG - JPG - GIF




Full fit result for \,|V_{cb}|
0.04237 \pm 0.00062
95% prob:[0.04117, 0.04367]
99% prob:[0.04057, 0.04428]
EPS - PDF - PNG - JPG - GIF




Full fit result for \,\alpha [^{\circ}]
87.4 \pm 6.2
95% prob:[75.7, 99.4]
99% prob:[71.5, 105.0]
EPS - PDF - PNG - JPG - GIF




Full fit result for \,\beta [^{\circ}]
23.7 \pm 2.6
95% prob:[18.8, 28.9]
99% prob:[16.6, 30.0]
EPS - PDF - PNG - JPG - GIF




Full fit result for \,\gamma [^{\circ}]
66.9 \pm 3.0
95% prob:[61.0, 73.0]
99% prob:[58.0, 75.9]
EPS - PDF - PNG - JPG - GIF




Full fit result for \,C_{B_{d}}
1.08 \pm 0.15
95% prob:[0.79, 1.40]
99% prob:[0.68, 1.61]
EPS - PDF - PNG - JPG - GIF



Full fit result for \,\phi_{B_{d}} [^{\circ}]
-2.8 \pm 2.8
95% prob:[-8.5, 2.7]
99% prob:[-11.5, 5.5]
EPS - PDF - PNG - JPG - GIF



correlations for \,\Phi_{B_{d}} - C_{B_{d}}



EPS - PDF - PNG - JPG - GIF




Full fit result for \,C_{B_{s}}
1.141 \pm 0.087
95% prob:[0.971, 1.320]
99% prob:[0.906, 1.431]
EPS - PDF - PNG - JPG - GIF



Full fit result for \,\phi_{B_{s}} [^{\circ}]
-0.026 \pm 0.959
95% prob:[-1.932, 1.899]
99% prob:[-2.896, 2.856]
EPS - PDF - PNG - JPG - GIF



correlations for \,\Phi_{B_{s}} - C_{B_{s}}



EPS - PDF - PNG - JPG - GIF




Full fit result for \,C_{\epsilon_{K}}
1.07 \pm 0.14
95% prob:[0.80, 1.38]
99% prob:[0.70, 1.59]
EPS - PDF - PNG - JPG - GIF




Fit Input for \,A_{SL_{d}}
Gaussian likelihood used
-0.0015 \pm 0.0017

EPS - PDF - PNG - JPG - GIF



Prediction for \,A_{SL_{d}}
-0.0028 \pm 0.0014
95% prob:[-0.0057, -0.0001]
99% prob:[-0.0069, 0.0010]
EPS - PDF - PNG - JPG - GIF




Fit Input for \,A_{SL_{s}}
Gaussian likelihood used
-0.0075 \pm 0.0041

EPS - PDF - PNG - JPG - GIF



Prediction for \,A_{SL_{s}}
-0.00033 \pm 0.00052
95% prob:[-0.00126, 0.00071]
99% prob:[-0.00152, 0.00123]
EPS - PDF - PNG - JPG - GIF

META TOPICMOVED by="DenisDerkach" date="1452332362" from="UTfit.ResultsSummer2015PreEPSSMNP" to="UTfit.ResultsWinter2016NP"
 
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