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QA plots/cuts for pure D 0

QA plots/cuts for pure D 0. reproduce pure D 0 sample (1 per event) with : 30 k with flat p T : 0<p T <5 10k with flat p T : 0<p T <10 Motivation : make plots with better statistic and extend the pT range to assure the trend observed for 0<p T <5 is identical for p T >5

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QA plots/cuts for pure D 0

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  1. QA plots/cuts for pure D0 • reproduce pure D0 sample (1 per event) with : • 30 k with flat pT: 0<pT<5 • 10k with flat pT: 0<pT<10 • Motivation : make plots with better statistic and extend the pT range to assure the trend observed for 0<pT<5 is identical for pT>5 • Some cuts I looked : • slength • dca product • DCA to PV • cospointing • Lxy/errLxy

  2. slength vs. pT (0<pT<10)

  3. slength vs pT (wrong sign,0<pT<10)

  4. slength vs. pT (0<pT<5)

  5. comments • Generate D0 up to pT = 10 GeV to have a better view of trend ; pure D0 • Observations : • Mean (filled symbols) and std (open symbols) of slength (TCFIT) increase with pT of D0 • Max (of mean) do not exceed (up to 10GeV) 600 microns --> set this as an upper limit • No dependence with probability • No dependence with wrong signs association

  6. dcaProduct of daughters

  7. comments • Daughters may have opposite signedDca to PV • Then a cut on negative product of DCA of daughters may select good D0 candidates • Obervations: • This cut reduces the # of candidates but from the fit, the chi2/NDF and rms are improved

  8. Dca (daughters) to PV

  9. comments • Apply a cut of min DcatoPV to select tracks that are NOT originating from the Primary Vertex • Observations : • Improvement when the cut is large (100 microns) • also tried an upper limit to remove tracks from kaons and hyperons (panel bottom right)

  10. Mass vs cospointing

  11. comments • Solid line : all associations ; red markers : good signs (K-π+) ; blue marker : wrong signs (K+π-) • Observations : • As seen before, the tails in the inv. Mass are coming from wrong signs associations • The cut on cosThetaPointing acts like a cut on the DCA of D0 to PV • For high values, the wrong signs contribution decreases

  12. Lxy/errLxy (from a thesis of HERA collaboration)

  13. details • In simulation, the primary vertex covariance matrix (C1) is null then I took the values from real data (x =y =z = 20 microns) • I have assumed that the crossed terms of the covariance matrices of primary and secondary vertices are null • For the secondary vertex(C2), as I don’t know the errors,I have tried by taking the double of C1 terms.

  14. Lxy/errLxy slength/dslength • slength/dslength is much broader than Lxy/errLxy • Due to the approximation I did for the primary and secondary vertices matrices • The plot on the right shows the inv. mass (pure sample) with no cut on good signs • Reminder : the wrong signs associations make the inv. mass broader • With the cut on Lxy/errLxy, this background is reduced

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