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Cross-sections (  barn)   ot (    ) PYTHIA (*) DPMJET (*) Minimum bias (  only) Minimum bias (  only) Single  (all) (  only) (all) (  only)

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Presentation on theme: "Cross-sections (  barn)   ot (    ) PYTHIA (*) DPMJET (*) Minimum bias (  only) Minimum bias (  only) Single  (all) (  only) (all) (  only)"— Presentation transcript:

1 Cross-sections (  barn)   ot (    ) PYTHIA (*) DPMJET (*) Minimum bias (  only) Minimum bias (  only) Single  (all) (  only) (all) (  only) 3<p T <45 GeV 38.422.881.238.147.249.335.1 36.721.977.836.644.747.032.6 4<p T <45 GeV 10.35.9820.39.4814.015.411.0 9.855.7319.59.1013.414.610.2 5<p T <45 GeV 3.562.046.513.045.245.294.32 3.421.966.252.925.004.964.0 6<p T <45 GeV 1.480.832.591.212.262.371.96 1.420.792.491.162.192.121.81 (*) Oren Y. and Nisati L. |  |<2.7 |  |<2.4 DPMJET default for rates computation (more conservative)

2 Computation of maximum travel length z max: –pionDecayerIDETlength = 3500; // mm // default 3500 r max: –pionDecayerIDETradius = 1200; // mm // default 1150 etaTr = (default) 1.67344 if (eta<etaTr) { maxTravel=pionDecayerIDETradius/sinTheta; } else { maxTravel=pionDecayerIDETlength/cosTheta; } Note: Trygve weigth computation is valid only in the barrel (eta<etaTr)

3 Procedure to compute cross- sections and compare histograms Min bias & single pion: –binWidth: Eta=0.05 Pt=0.5GeV –minBiasXsec=80mbarn –multiplicity = nPrimary/n9999 (pi/K close to vertex divided mothers close to vertex with status 9999) For single pion is 1, for minbias around 2-3 for pion, 3-4 for kaons Depends on eta, pT cut Close to vertex means |R|<0.05 z<150 –correction = # of pion selected per minbias events wrt minbias For minbias is 1, for single pion 0.38 (averaged from the whole pT vs eta plot, so not depending from cuts while it should depends  systematic error) –totSampleEvents reweighted for decay probability (but I am not reweighting minbias events with kaon weight: to do) singlepions=953940 minbias=113670

4 Procedure to compute cross- sections and compare histograms scale is used to rescale histograms and compare them –scale = totSampleEvents / correction / multiplicity / minBiasXsec integral=events selected crossSection = integral / scale scale = scale * binWidthPt * binWidthEta


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