1 In the meantime Varied p T cut (1.5 GeV, 1 GeV, 500 MeV) 2. Allowed for events with 2 good tracks only (+  ), originally 4 good tracks where required, in order to fully reconstruct the B S  D S (  )  decay 3. Enlarged angle cuts ,  between K + and K  : 10  13 deg (see next slide) 4. changed from CDF fitter to VKal fitter 5. Used reprocessed data (r1093) 6. Applied the analysis to MC Min.Bias sample

2 What we presently do/plan to do  Do background subtraction by using same sign combinations of tracks.  Find more appropriate function for describing the background shape (polynomial has stability problems with low statistics).  The fitting function for signal should better be a Breit- Wigner accounting for phase space convoluted with a Gaussian to represent the detector resolution.  Go down only to p T > 0.8 GeV, and build a phi peak again (still no dE/dx). Make a mass plot of (   ) candidates, that build a 3-prong vertex, look at the D S  meson mass region (putting both + and  candidates into one plot). last time we said:

3 Width of the  (1020) in MC truth width [GeV] mass [GeV] PDG Pythia_ ( current version)PMAS(KC,2)PMAS(KC,1) Fitting the Monte Carlo truth  signal with a Breit-Wigner (relativistic and non-relativistic) gives m =  GeV and  = 4.45  0.01 GeV, which is larger than the current PDG value  investigations Conclusion:  (1020) in ATLAS Monte Carlo productions not generated with the current PDG value for the width!

4 Fit

5 Fitting the  (1020) signal Using a convolution of a Breit-Wigner with a Gaussian (and a threshold function for the bg)  inside the RooFit framework  has already implemented this convolution: “Voigtian“ –fix width of Breit-Wigner (  = 4.26 GeV) RooVoigtian signal("signal","Voigtian PDF",x,mean,width,sigma); width.setVal(4.26); width.setConstant(kTRUE); RooGenericPdf bg("bg","background","(x )^p*exp(-b*(x ))",RooArgSet(x,p,d)); RooAddPdf model("model","sum of signal and bg",RooArgList(signal,bg),RooArgList(Nsig,Nbkg)); model.fitTo(data); // Extended Maximum Likelihood Fit (unbinned) width.Print();

6 Comparision with the old method N  = 60.7  15  = 2.93  0.74 N  = 58  16  = 2.88  0.76 N  = 76  19  Gauss = 1.8  1.0 One gets 30% more events (due to tails of Breit-Wigner). with first plot shown (p T > 1.5 GeV) Gauss Voigt