Difference: MassFitting (9 vs. 10)

Revision 102010-09-24 - LarsEklund

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G-Fact Mass Fitting

Validation of signal fraction fitter (Lars)

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True S.F. Initial value Mean [%]
Sigma [%]
Pull mean Pull sigma
Bd->π+π- 8.47
10 8.45 0.13 -0.13+/-0.03 1.02+/-0.02
Bd->K+π- 17.82
15 17.84 0.14 0.12+/-0.03 0.99+/-0.03
Bd->K-π+ 14.58 15 14.58 0.13 -0.02+/-0.03 1.01+/-0.03
Bs->K+K- 8.47
10 8.47 0.10 0.01+/-0.03 1.02+/-0.03
Bs->K+π- 1.62 1 1.62 0.07 -0.03+/-0.04 0.96+/-0.03
Bs->K-π+ 0.72 1 0.71 0.05 -0.32+/-0.03 1.01+/-0.02
Bd->π+π-π0 15.0 10 14.98 0.16 -0.11+/-0.03 1.02+/-0.02
Combinatoric 33.32 38 35.02


Sensitivity to number of events

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A series of fits to the data set Mix_Bd2pipiKpiNBs2KK (above) was done with a decreasing number of events. The initial values and fit configuration was identical to the fit described above. The results are summarised in the following graphs.

The error from the fit as reported by MINUIT versus number of events is show in this graph.

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A series of fits to the data set Mix_Bd2pipiKpiNBs2KK (above) was done using a decreasing number of events, from 100 000 to 100 events. The initial values and fit configuration was identical to the fit described above. The results are summarised in the following graphs.
  • The error from the fit as reported by MINUIT versus number of events is show in this graph. As expected, the statistical error from the fit follows well the 1/sqrt(N) law. The unit here is % signal fraction, hence 1% error means that the signal fraction Bd -> pi pi is estimated to be 24 +/- 1 %.
  • The mean of the pull distribution is shown in this graph. The mean is less that 0.1 apart from the fits with the largest number of events. Since the statistical error is very small (<2*10^-3 for 100k evt) even the smallest bias is seen. At 100k event the observed absolute bias is < 4*10^-4.
  • The bias in absolute numbers is shown in this graph. This is calculated as the statistical error of the fit times the mean of the pull distribution. Hence this gives the bias in absoulte units. The bias is less than 0.1 % if more than 1000 events are used, below that number a measurable bias is seen.
  • The sigma of the pull distribution is seen in this graph. Seems to be pretty independent of the number of events.
 

Paul's study with the signal fraction fitting

Just start typing here paul....This is a link.

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META FILEATTACHMENT attachment="Mix4_Bs2KK_pull.png" attr="" comment="" date="1285081885" name="Mix4_Bs2KK_pull.png" path="Mix4_Bs2KK_pull.png" size="23849" stream="Mix4_Bs2KK_pull.png" tmpFilename="/usr/tmp/CGItemp29124" user="LarsEklund" version="1"
META FILEATTACHMENT attachment="FitErrorVsNEvents.pdf" attr="" comment="" date="1285240808" name="FitErrorVsNEvents.pdf" path="FitErrorVsNEvents.pdf" size="9257" stream="FitErrorVsNEvents.pdf" tmpFilename="/usr/tmp/CGItemp27693" user="LarsEklund" version="1"
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META FILEATTACHMENT attachment="MeanOfPullVsNEvents.pdf" attr="" comment="" date="1285319161" name="MeanOfPullVsNEvents.pdf" path="MeanOfPullVsNEvents.pdf" size="8824" stream="MeanOfPullVsNEvents.pdf" tmpFilename="/usr/tmp/CGItemp27401" user="LarsEklund" version="1"
META FILEATTACHMENT attachment="SigmaOfPullVsNEvents.pdf" attr="" comment="" date="1285319168" name="SigmaOfPullVsNEvents.pdf" path="SigmaOfPullVsNEvents.pdf" size="8956" stream="SigmaOfPullVsNEvents.pdf" tmpFilename="/usr/tmp/CGItemp27469" user="LarsEklund" version="1"
META FILEATTACHMENT attachment="AbsBiasVsNEvents.pdf" attr="" comment="" date="1285319173" name="AbsBiasVsNEvents.pdf" path="AbsBiasVsNEvents.pdf" size="9420" stream="AbsBiasVsNEvents.pdf" tmpFilename="/usr/tmp/CGItemp27422" user="LarsEklund" version="1"
 
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