1. Search for High-Mass Resonances in e + e - Jia Liu Madelyne Greene, Lana Muniz, Jane Nachtman Goal for the summer Searching for new particle Z’ --- a massive gauge boson in Proton-antiproton collision at CDF

2. Summary of analysis  Signature-based search for resonance in e+ e- mass spectrum  Hypothetical new particle (Z prime) decaying to e+ e-  Reconstruct its mass -- look in high mass region  We are starting with existing code, analysis method from previous analysis  His analysis – 1.3/fb; ours – 2/fb  Requires understanding and running his code, validating new data  We are now focusing on CC( two electron in the Central detector), but we have starting on CP( one Central, one plug electron)  Main pieces of analysis  Selecting electrons  Understanding composition of e + e - sample  Scan mass spectrum, look for bump (quantify probability)  Limits on Z’ production

3. Signature-based search for resonance in e+ e- mass spectrum that gives evidence for a new particle Reconstruct its mass-- We expect it to be high-mass (hundreds of GeV/c2) due to previous searches

4. Run period for the 4 data set 0i, p9,p10 and p11 0iP9 (pb-1) p10 (pb-1) P11 (pb-1) Luminosity 468192 +/- 12276 +/- 17239 +/- 14 Run range 203800-222600222529-228596228664-237795233200-237800 Total Dataset (including 0d and 0h data) = 2 fb-1 Sam’s analysis through p8(0i) used 1.3 fb-1  In today’s talk we use 4 datasets: 0i for comparison, p9, p10,p11( in progress) is the new data which we validate

5. Checking the new data  Previous analysis covered up to p8 (0i data)  We want to extend the analysis through p11, using the same code, same MC and scale factors  Validate the new data  --check the electron ID distributions  --Check mean and sigma for Z  --Check number of Z

6. Event Selection  Events are required to have one electron in the central region and another in either the central or plug regions  Two channels, CC and CP  Use both CC and CP  Pros and Cons of CP electrons  Find more Z’ particle  Adds angular acceptance  Limited tracking information  Contribute more fakes  Central electrons must pass the identification cuts shown next

7. CEM Selection Cuts VariableTight CC (CEMCC) Region= CEM FiducialFid = 1 or 2 ETET ≥ 25 GeV Track Z 0 ≤ 60 cm Track P T (E T <100GeV)≥ 15 GeV/c Track P T (E T ≥100GeV)≥ 25 GeV/c Had/em≤ 0.055 + 0.00045 x E Isolation E T ≤ 3 + 0.02 x E T GeV L shr Track≤ 0.2 E / P(E T <100GeV)≤ 2.5 + 0.015 x E T GeV E / P(E T ≥100GeV)Track P T ≥ 25 GeV/c CES ∆Z≤ 5.0 cm CES ∆X≤ 3.0 cm PEM is on the way…. These are the standard cuts used for electron ID with some modifications made by previous search to account for very high ET events.

8. Electron ID  Had/em The ratio of the total hadronic to total electro-magnetic energy of all the towers composing the cluster  Isolation  the sum of the hadronic and electromagnetic transverse energies in a cone of 0.4 radius centered on the cluster with the electron and leakage transverse energies subtracted off  Isolation Et is corrected for multiple interactions by subtracting 0.35 GeV or 0.27 GeV per additional vertex for data and Monte Carlo respectively.  Lshr Track Lateral Shower Sharing Variable. A measure of how well the energy deposits in the adjacent towers matches that expected for an electromagnetic shower.  E/P The transverse energy of the electron divided by the track p T  CES ∆Z The difference between the z position of the highest pT beam-constrained track extrapolated to the CES plane and the z position of the electromagnetic shower as measured by the CES.  CES ∆X The difference between the x position of the highest pT beam-constrained track extrapolated to the CES plane and the x position of the electromagnetic shower as measured by the CES.

9. Validation Plots

10. Efficiencies of electron ID variable for each dataset 0i dataP9 dataP10 data Had/em.992±.0008.992±.001 Isolation.974±.001.977±.002.973±.002 Lshr track.99±.0009.989±.002.987±.001 E/P.9996±.0002.9997±.0003.9997±.0002 CES ∆Z.9977±.0005.9971±.0009.9971±.0007 CES ∆x.993±.0008.992±.001.993±.001

11. Total Efficiencies Run period 0ip9p10p11MC Total Efficiency 0.925 +/- 0.002 0.931+/ - 0.004 0.925 +/- 0.003 0.9## +/- 0.00# 0.943 +/-.0003 The efficiencies for each run period agree within statistical error. Therefore, we can continue to use the Scale Factors calculated and the Monte Carlo used for the 0i calculations.  We checked the each ID variable for each run period

12. Check Z peak position and width  Subdivide data into smaller run periods  Fit z peak, extract mean and width  The reason for checking mean and sigma 1) Z peak mean: verifies electron energy calibration 2) Z peak Sigma: verifies momentum reconstruction

13. Example Z peaks of cc from 0i period from the fit

14. Example Z Peaks of cc from P9 data

15. Example of Z peaks of cc from P10 data

16. Z mass mean value of cc for 0i, p9 and p10 data 0i data P9 data P10 data

17. Z mass sigma value of cc for 0i, p9 and 0i data 0i data P9 data P10 data

18. Example of Z peaks of cp from 0i data

19. Example of Z peaks of cp from P9 data

20. Example of Z peaks of cp from P10 data

21. Z mass mean value of cp for 0i, p9 and p10 data 0i data P9 data P10 data

22. Z mass sigma value of cp for 0i, p9 and p10 data 0i data P9 data P10 data

23. Checking the Number of Z  Count number of Z’s reconstructed in each subdivided run period Calculate the N/L for each run period that used to check the mean and sigma  This checks 1)the detector ( including trigger) operate, 2)electron reconstruction

24. Number of Z 0i data P9 dataP10 data

25. Finding New Physics in the dielectron mass spectrum  We expect a narrow resonance, but how do we tell a real peak from a statistical fluctuation?  Look at poisson probability for the expected number of events to fluctuate to the number observed or higher Z Possible Z’

26. Example from Monte Carlo background, with no signal:  Look at expected vs observed  Example to show method: MC with no signal  Calculate probability to observe N_observed or more Input distribution expectedobserved Probability to observe N_observed or more events

27. Goal: less model-dependent search  Scan mass range, calculate probability assuming no signal, take into account number of bins searched  Produce plot such as was done for previous analysis 

28. Search for Z’  Using Sam’s simple program to calculate probability of Z’ in the data  requires input :  Data, MC signal, background distributions (nominal and errors)  Will extend to full Z’ mass spectrum Data Background Signal M Z’ = 300

29. Summary  We are updating Sam Harper’s analysis to 2 fb- 1 using his code, method  We are validate our implementation using old data  We add p9, p10, p11( in progress)  We will scan the Di-electron mass spectrum  We are understanding the output probability and limit code  Maybe we will see something new? Or, set limits on Z’

30. ThAnk YoU ^@^

31. N-1 Efficiencies  We calculated the efficiency of each individual cut (N-1 Efficiencies)  E i N-1 = 2 x N TT N TT + N i N-1  where N TT is the number of events with both legs passing all tight cuts and N i N-1 is the number of events with one leg passing all tight cuts and the other leg passing all tight cuts except the i th cut.