PhD thesis of Renata Kopečná Angular analysis of B+->K*+(K+pi0)mu+mu- decay with the LHCb experiment
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  1. \subsection{Systematical uncertainties}\label{sec:trackEff-sys}
  2. The sources of potential systematic uncertainties have been investigated for the \runI track reconstruction efficiency measurement~\cite{TrackEffRun1}. The method of measuring the track reconstruction efficiencies remained unchanged, hence the uncertainties are not expected to significantly change in the \runII measurement.
  3. Changing the \jpsi mass signal model from the sum of two Crysta-ball distributions to the sum of two Gaussian distributions does not change the efficiency significantly compared to the statistical uncertainty. Similarly, changing the background model from an exponential distribution to a linear one leads only to a negligible change in the track reconstruction efficiency.
  4. Another source of the systematical uncertainty could be the difference between the long method and the combined method. However, the difference is observed to be small relative to the statistical uncertainty and is further reduced in the ratio of the track reconstruction efficiency in data to the efficiency in the simulation.
  5. The dominant systematical uncertainty in the \runI measurement originates from the choice of the occupancy variable used to improve the agreement of the simulated event sample with the real data. The uncertainty is evaluated by using the number of hits in
  6. the \spd, the number of long tracks in the event and the number of primary interaction vertices as the occupancy variables. The largest deviation observed in \runII for the correction factors obtained from the combination of all methods in any of the two-dimensional correction tables is 0.8\%.
  7. %8 Systematic uncertainties
  8. %Small differences in the ratio of efficiencies are seen when reweighting the simulated
  9. %samples in different parameters such as the number of primary vertices, or the number of
  10. %hits or tracks in the different subdetectors. The largest of these differences is taken as
  11. %a systematic uncertainty and amounts to 0.4%. No systematic uncertainty is assigned
  12. %for the agreement of the track reconstruction efficiency determined by the tag-and-probe
  13. %method and the hit-based method (which is on the order of 1%), as the differences cancel
  14. %when forming the efficiency ratio. Accordingly, no systematic uncertainties are assigned
  15. %for the fit model as these cancel when forming the fraction of reconstructed J/ψ decays
  16. %where the probe can be matched to a long track. It has been checked that this is true
  17. %for a range of fit models, the largest variation being 0.2%. Furthermore, no systematic
  18. %uncertainty is assigned to the possible matching of a correctly reconstructed probe track
  19. %to a fake long track, as the requirement for a large overlap in the subdetectors ensure that
  20. %both reconstructed tracks are either real tracks or fake tracks, where the latter would not
  21. %peak at the J/ψ mass. No systematic uncertainty is assigned for the fact that the VELO
  22. %+ T-station method and the long method show slightly different results in Figs. 4–6, as
  23. %both methods probe different momentum spectra and any residual difference will cancel
  24. %15
  25. %when forming the ratio with simulation. No systematic uncertainty is assigned for the
  26. %double-counting of the matching efficiency in the combined method, as this efficiency is
  27. %very close to 100%, and any uncertainty would get further reduced when forming the ratio
  28. %with simulation. No systematic uncertainty is assigned for the large difference for the
  29. %VELO + T efficiency between simulation and data at low momenta in 2011 and 2012, as
  30. %this is automatically taken into account when forming the ratio of efficiencies. Despite this
  31. %difference, the integrated track reconstruction efficiencies between simulation and data are
  32. %in agreement due to compensation of this effect for high momenta, where the efficiency is
  33. %higher in simulation than in data