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Dilepton Mass. Progress report.

Dilepton Mass. Progress report. Peter Renkel: Southern Methodist Uni. dileptoners. Contents. L+track selection ( frosen in January) NuWt reminder PDH (around August for the ICHEP) Pros. PDF+PDH combination in review for p17/PRD Talk at TOP2008 at Elba. May 22. l+track selection.

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Dilepton Mass. Progress report.

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  1. Dilepton Mass. Progress report. Peter Renkel: Southern Methodist Uni. dileptoners

  2. Contents • L+track selection (frosen in January) • NuWt reminder • PDH (around August for the ICHEP) • Pros. • PDF+PDH combination in review for p17/PRD • Talk at TOP2008 at Elba. May 22

  3. l+track selection • Dimuon and lepton+tau vetoes. • Trigger ORing. • Final JES. • Require: • Lepton, track, at least 2 jets • Leading jet pT>40 GeV • Second jet pT>20 GeV • Met, ZFitMet<20 inside the 70-110 GeVwindown and Met, ZFitMet<15 outside this window. For getting Kz factors. • Invert cuts for the Control Plots.

  4. Final selection. • Require: • Met, ZFitMet>35(e+track)40(mu+track) inside the 70-110 GeV window and Met, ZFitMet>25(both channels) outside this window. • NN medium b tag. At least one tag is required.

  5. Here should be plots, but you have them in the note/PRD

  6. Reminder. Neutrino Weighting (NuWt) • -1C fit underconstrained fit – Assume top mass – can solve event • Assign a weight to each event: • Omit the information on missing momenta • Sample from expected neutrino rapidity spectrum • Compare calculated and observed Etmiss and assign a weight: • Repeat for all test masses • Get a weight distribution per event

  7. Templates or PDH Event Single Event Weight Distribution Weight Distribution Mean=200 GeV RMS=25 GeV For each event record two first moments: mean and rms. Create a 2Dim histo Probability Distribution Histogram (PDH) PDH (templates)

  8. Fit procedure • We fit our histograms in NuWt to smooth functions to avoid local fluctuations • 3Dim signal (input top mass, mean, rms)  3Dim analytical function • 2Dim background (mean, rms)  2Dim analytical function A mean vs. rms slices of the 2d plot (PDH) and fit function (PDF) PDH PDF

  9. Likelihood distribution • Get a likelihood distribution by fixing moments obtained from data in the 3-Dim/2-Dim distributions • The moments are taken from data smoothed function (PDF) templates MC DATA fixing, taking a slice meani, rmsi

  10. PDF related questions • Very difficult to fit • Signal: 3 – dimensional functional form (mt, mean, rms) • 13 parameters in the fit • BG: All BG have different shapes/functional forms. • Can approximate with gaussians each, but if there are several of them – many gaussians, quite complicated. • Lots of time/resources • Is our fit function (PDF) the optimal one? Does it create any bias? • Yes, ensemble tests are Ok, but anyway it’s good to check

  11. PDH method • Why not to use PDH for check? • Seems as drawback, since we invented PDF method to smooth local fluctuations • Are these fluctuations important? • Let’s check.

  12. PDH method • Use UNSMOOTHED histograms (PDH) as templates • Modify: • No fitting • When reconstructing mass, get non analytic function, which we have to fit (simple parabolic fit). Non analytic function – parabolic fit smoothed function templates ! MC DATA fixing, taking a slice meani, rmsi

  13. Simple check. 3 random ensembles PDF method PDH method

  14. Improvements. Filling zero bins. PDH(mean,rms)=0 -logL=inf bad fits PDH PDH coorected bins mt mt

  15. Improvements. Filling zero bins. PDH(mean,rms)=0 -logL=inf bad fits PDH PDH coorected bins mt mt

  16. Improvements. Extended range of top masses. new points new points Added: 110, 125, 140, 215, 230 GeV samples

  17. Ensemble tests PDF PDH before PDH after 5.82 12.95 5.15 Stat error Pull distribution

  18. PDF vs. PDH • PDF smoothes local fluctuations. • PDH from the other side is sensitive to the local fluctuations. • But it can catch peculiarities of the signal, smoothed out by the PDF. • PDF and PDH add some information to each other.

  19. PDF – PDH. GeV 85% correlation <PDFi PDHi> r= =85% σPDF σPDH

  20. Results • Gain – 100% (299 out of 300) ensembles have fit (compared to 90% before) • Slopes and offsets are better. • Results. Fixed systematic for the PDH • Combined result (BLUE method) PDF PDH We received comments from Ulrich. Thank you! Looking at them.

  21. Combination

  22. Conclusions • Alternative method is designed • Sensitivity, comparable to PDF • Simpler, automatic, gives some additional information • Combine and get a combination.

  23. PDH Status now • Method implemented in 2 weeks! Compared to half a year for the fits in PDF. Automatic! • Was easy to run with just one variable mean and show, that mean + rms 2d templates give ~16% improvement compared to 1d mean templates. • Similar fits for PDH would take several months of work • Started as a simple cross-check, eventually all chain is done. • Gives comparable result and comparable systematic uncertainty

  24. 85% correlation For some of ensembles PDF and PDH errors are equal. The combination gives 5% improvement For the bulk of ensembles, the errors vary by ~ 10% difference the ‘combination’ is very close to the Min(PDF,PDH). ~10% improvement in mean over all ensembles. If take minimum of two measurements. Combination Combination Minimum mean=4.8 GeV mean=4.7 GeV

  25. Empty bins A 1d slice at fixed mean0 PDH PDH in mt/mean plane mean0 from data mean1 from data mt 180 175 160 165 170 175 180 mt A 1d slice at fixed mean1 170 PDH 165 160 mean 160 165 170 175 180 mt

  26. Smoothing empty bins. Default approach PDH PDH 123 61 coorected bins 35 12 mt mt

  27. Smoothing empty bins. Comparison with default approach • Uncertainty due to smoothing of empty bins in PDH. • If several empty bins at the edge, normalize to their number. • If several empty bins are surrounded by non – empty bins, then: • If at least one of them has exactly 1 entry, normalize to their number. • Shift of 0.1 GeV observed PDH PDH 1 1 PDH PDH mt mt mt mt

  28. Future • Should smooth PDH, but not with analytic functions. Taking a bin, account for neighbors (also automatic). • Reason – more stable fits • Should be easy ( change one line of code – see below) improved PDH h PDH1 = h1 IMPROVED_PDH~(h0+h1+h2)/3 PDH 0 1 2

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