1. Cocktail algorithm studies Carmen Diez Pardos Silvia Goy López CIEMAT Madrid Muon POG 07/04/2011 1C. Diez Pardos

2. Introduction: cocktail algorithm Study in detail chi2, resolution and pulls for the data and MC used in the W' analysis Not many high pt muons Also: compare with cosmics (next step) There are several tunes, the used one is “TuneP” Algorithm logic: selection based on -lnProbchi2 Picky is taken as default, if it doesnt exit -> Take TPFMS -> tracker ->global If -lnProbchi2 (Picky-Tracker) >30, chosen tracker If -lnProbchi2 (TPFMS-chosen) >0 : takes TPFMS 2C. Diez Pardos

3. Samples and Muon Selection Samples:  Data: 2010RunB Nov4ReReco  MC: Fall10 W samples, W’ (1500GeV) Selection on the criteria for the analysis  Must be Global and Tracker Muons  Combined isolation: < 0.15 in a cone R < 0.3  Quality cuts related to the track: d0 0, number of valid tracker hits >10, number of matching segments >1, valid muon hits >0  Muon matched to a L3 muon: HLT_Mu9, _Mu11, _Mu15 Note: For this selection W MC is not enough to describe data (should include other BG), but it should be fine for p T > 100 GeV. 3C. Diez Pardos

4. Cocktail choice for data and MC In general, TPFMS preferred, for data Picky in the Barrel until pt 200 4C. Diez Pardos  At low pT data and MC differ in Barrel and EC:  Low pt W: more TPFMS  Data: more Picky (changes tendency at high pT, see next slide)  TkOnly contibrutes ~1%

5. Cocktail choice for data MORE TPFMS ACORDING TO joRDAN

6. -LnProb(chi2) difference TPFMS- PICKY Negative mean! W W W' W' Mass 1500 GeV BARREL ENDCAP 6C. Diez Pardos

7. Ratio chi2 MC W over Data pt>100 GeV In this region most of the BG is W, distributions are normalised to area o 7C. Diez Pardos

8. Ratio ln chi2 tail prob MC W over Data pt>100 In this region most of the BG is W, distributions are normalised to area TPFMS 8C. Diez Pardos PICKY

9. Pt distributions ANIADIR RATIO, PT for pt<100 W 9C. Diez Pardos

10. Resolution residuals for the chosen algo Left tail: reconstructed momentum higher than generated Interested: See tails in low pt distributions and resolution core in high pt MEAN: Similar? SIGMA: Quite similar within algorithm, better for the chosen one Fit for each algorithm the resolution, for the whole region and the BARREL ONLY?? PUT a plot 10C. Diez Pardos

11. Resolution residuals for the chosen algo Left tail: reconstructed momentum higher than generated Interested: See tails in low pt distributions and resolution core in high pt MEAN: Similar? SIGMA: Quite similar within algorithm, better for the chosen one Fit for each algorithm the resolution, for the whole region and the BARREL ONLY?? PUT a plot 11C. Diez Pardos

12. Resolution residuals for the chosen algo Left tail: reconstructed momentum higher than generated Interested: See tails in low pt distributions and resolution core in high pt MEAN: Similar? SIGMA: Quite similar within algorithm, better for the chosen one Results separated by selected or rejected for Picky and TPFMS PONER % de picky y TPFMS Por ver que mejor no cogerlo nunca?? ==> Hacer el del cocktail total 12C. Diez Pardos

13. Resolution residuals as a function of pt for W' MC Tracker not shown, too little stat 13C. Diez Pardos

14. Resolution residuals as a function of pt for W (allpt!) MC 14C. Diez Pardos

15. Pulls as a function of pt for W MC 15C. Diez Pardos

16. Pulls as a function of pt for W' MC 16C. Diez Pardos

17. Conclusions - How valid a tune with data/MC is applicable to the other samples: Different behaviour in eta regions and pt Datos: falta una contribucion para pt bajo de MC - Is it the optimal cocktail? (It seems that it works fine...) - Check with cosmics (Jordan?) Many, many thanks to Jordan for all the help and pacience 17C. Diez Pardos

18. Back-up 18C. Diez Pardos

19. Chi2 between data/MC (all BG) (Plots by G. Abendi, A. fanfani) 19C. Diez Pardos

20. Barrel Choice for MC and data

21. Data: all eta regions