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PPR Meeting - May 16, 2003 Andrea Dainese 1 Measurement of D 0 production in pp Andrea Dainese University of Padova

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PPR Meeting - May 16, 2003 Andrea Dainese 2 Outline One step back: study of feed-down from beauty (b B D 0 ), important also for systematic error Estimation of statistical error on dN(D 0 )/dp T Estimation of systematic errors: contribution from beauty uncertainty on D 0 c MC corrections & uncertainty on B.R. to K Combination of errors and error on total cross section PHYSICS PERFORMANCE: SENSITIVITY TO

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PPR Meeting - May 16, 2003 Andrea Dainese 3 Feed-down from Beauty (1) At LHC energies Fraction of D 0 from chain b B 0 /B + D 0 not negligible Standard NLO pQCD + Pythia fragmentation + PDG: Important to use the correct ratio D from b / D from c: Selection may change the ratio Results have to be eventually corrected for feed-down estimate systematic error from b cross section

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PPR Meeting - May 16, 2003 Andrea Dainese 4 Feed-down from Beauty (2) How D 0 signal was generated: generate standard pp minimum bias with Pythia keep only those events that have a D 0 weight D 0 s according to their p T in order to reproduce distribution given by NLO pQCD In that sample: factor 4 too high! All candidates have been re-weighted: c D: to reproduce p T distr. by NLO b D: to have correct ratio (5.5%) AND reproduce THEIR p T distr. by NLO (harder than for c D !)

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PPR Meeting - May 16, 2003 Andrea Dainese 5 Feed-down from Beauty (3) After event reconstruction (tracking + vertexing): D 0 from b / D 0 from c = 5.8 % Different p T slopes After selection (impact parameter + pointing angle): D 0 from b / D 0 from c = 16 %

6 PPR Meeting - May 16, 2003 Andrea Dainese 6 Feed-down from Beauty (4) Selection on pointing angle expected to suppress D 0 from b (they point to the decay vertex of the B and not to the primary vertex) Resolution on pointing angle is worse for D from c pointing angle enhances D from b D D B V1V1 V1V1 K K 2

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PPR Meeting - May 16, 2003 Andrea Dainese 7 Feed-down from Beauty (5) Also selection based on impact parameter enhances beauty 20% ! Systematic error from b = uncertainty on b cross section fraction of D 0 from b D 0 from b / D 0 from c = 16 %

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PPR Meeting - May 16, 2003 Andrea Dainese 8 Feed-down from Beauty (6) new cut |d 0 |< 500 m can suppress beauty impact parameter distribution after selection D 0 from b / D 0 from c = 10 %

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PPR Meeting - May 16, 2003 Andrea Dainese 9 Results Essentially unchanged w.r.t. to last shown (March) K id

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PPR Meeting - May 16, 2003 Andrea Dainese 10 Binning & Smoothing in dN/dp T Optimize binning in the range 0-14 GeV/c: bin size increasing with p T good precision on measurement of p T slope small statistical error up to high p T Fit p T distributions to remove fluctuations due to reduced statistics of simulation at high p T (especially for Bkg) Use fitted function to obtain smooth dN/dp T for S and B

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PPR Meeting - May 16, 2003 Andrea Dainese 11 Statistical Error (1) Rel. Statisitical error on S expected to be 1/Significance: S = (S+B) observed – B estimated error on (S+B) observed = sqrt(S+B) error on B estimated negligible (uses info from wide range mass distr.) error on S = error on (S+B) observed = sqrt(S+B) relative error on S = sqrt(S+B)/S = 1/Significance This has been checked by generating and fitting a large number of invariant mass distributions, using as input parameters the results of the simulation, namely: statistics for S and B VS p T width of D mass distribution VS p T slope of exponential bkg VS p T

12 PPR Meeting - May 16, 2003 Andrea Dainese 12 Statistical Error (2) 0.5

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PPR Meeting - May 16, 2003 Andrea Dainese 13 Systematic Errors When the data are corrected for efficiency, acceptance, etc… systematic errors are introduced Main sources of systematic errors have been considered CORRECTIONERRORS INTRODUCED subtraction of contribution from buncertainty on b cross section selected reconstructed (MC correction) error on D 0 c correction for PID efficiency errors from MC description of ALICE (TOF eff. & contaminations) extrapolation from D 0 reconstructed to D 0 produced in |y|<1 errors from MC description of ALICE (tracking eff. & resolutions) from D 0 K to total D 0 error on B.R. D 0 K ~10% 2.4%

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PPR Meeting - May 16, 2003 Andrea Dainese 14 Systematic from b subtraction Fraction of D 0 from b will be estimated from MC and subtracted to obtain D 0 from c Input to MC: Beauty cross section in pp will be measured by all 4 expts Now: pessimistic case use theoretical uncertainty (80%) Error on N(c D 0 ) = 0.8 N(b D 0 ) Relative error = 0.8 N(b D 0 )/N(c D 0 ) % = 8% present th. uncertainty

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PPR Meeting - May 16, 2003 Andrea Dainese 15 Systematic from c = ( ) m From selected to reconstructed D 0 : For each D, calculated equivalent true value of ct = L M/p depends on c in MC input beauty assuming it is primary charm lower cut on d 0 upper cut on d 0 error ~ 0.5 % negligible

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PPR Meeting - May 16, 2003 Andrea Dainese 16 Errors combined Note: Error from MC may be lower than 10% and dep. on p T Error from b may be much lower after direct measure Error on BR doesnt alter p T shape dN(D 0 )/dy for |y| 0.5 GeV/c (91% of p T > 0) statistical error = 3 % systematic error = 13 % from b = 8 % MC correction = 10% B.R. = 2.4 %

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PPR Meeting - May 16, 2003 Andrea Dainese 17 Comparison with CDF-Run II CDF Run II: preliminary measurement of charm cross section using the Secondary Vertex Trigger sigma(D 0, p T >= 5.5 GeV/c) = (13.3 ± 0.2 ± 1.5) µb 1.5% 11% We could measure the charm cross section in LHC from p T > 0.5 with the ~ same precision/accuracy

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PPR Meeting - May 16, 2003 Andrea Dainese 18 Conclusions Feed-down from b studied – important for systematic error Statistical errors on p T distribution estimated Main systematic errors considered Physics performance on charm X section measurement in pp: statistical error: 3 % systematic error: 13 % (only indicative value) Systematic error from MC corrections will dominate Next steps: try D 0 impact parameter to measure (b D 0 ) / (c D 0 ) a la CDF sensitivity for comparison with NLO pQCD and/or Pythia error analysis for Pb-Pb play a bit with comparisons (R AA (p T ) ….)

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