Top quark identification and its mass measurement at CDF

1. Top quark identification and its mass measurement at CDF Yen-Chu Chen Institute of Physics, Academia Sinica IPAS 2003/11/12

4. The Top Quark • Evidence in 1994 (CDF) • Observation in 1995 (CDF&D0) • RUN I : • Consistent with the SM But … • RUN II : • Higher energy and luminosity • Better detector • Preliminary results on cross section, mass, etc. In the next few years Top physics can be done only at the Tevatron! Run II will be ~50 times better than Run I!

5. Higher luminosity • Energy: • P : 900 GeV/c  980 GeV/c • S : 1.8 TeV  1.96 TeV • Bunch crossing: • 6 x 6  36 x 36 • Luminosity: • 3.3/9.1  1030  30  1031 • ( Ia/Ib ) ( 4  1031 for now )

6. Better detector

7. CDF RUN I Detector

8. Top physics  q' l  q _ Decay modes, rare decays, etc. W  Production Cross section b t  P P   X b t Top polarization Top mass W  W helicity _  q' l  q _

9. CDF/D0 2 fb-1goal!

10. Large Pt’s for leptons, neutrinos and jets. Large Pt for neutrino means large missing ET. Even event topology: central and spherical At least two b jets. Top event identification

11. 69.7

12. Event selection: Lepton ID Lepton Pt > 20 GeV/c Isolated leptons Di-lepton invariant mass (exclude events of 75 GeV -> 105GeV) Missing Et > 25 GeV  > 20o (for missing Et < 50 GeV) 2 jets Ht > 200 GeV Opp. Sign Overall efficiency including geometry and trigger was 0.541  0.0013 %, for 2002 Winter conferences! Better efficiency, ~1%, had been achieved for 2003 Summer conferences! Top event identificationDi-lepton

13. Top Mass Measurement, RUN I Run I summary • Template method: • Kinematic fit for t t • Combinatorial issues •  best c2 combination • Likelihood fit • Dynamical method: • Event probability of being t t as a function of m(t) • Better use of event information  increase statistical power • Well measured events contribute more • New D0 Run I result: • factor 2.5 improvement on the statistical uncertainty! – – D0 l+jets

14. People: Jaroslav Antos, Andy Beretvas, Yen-Chu Chen, Miroslav Siket, Roman Lysak, Gong Ping Yeh Top mass measurementDi-lepton _

15. The challenges: • Neutrinos are not directly measured. • Combinatory issue. • Based on kinematics: energy momentum conservation • 24 unknowns with only 23 constraints • Add in Pt, z Pt, z Pt t, z 0 • E2 = P2 + M2, etc. • P1,x + P2,x = ET,x, P1,y + P2,y = ET,y • Pb + PW = Pt, etc. • Mt = Mt • MW = 80.4 GeV _ _ / / _

16. There maybe two solutions: Pick the one having the smallest t t pair invariant mass. Fit the template with Landau like function + Gaussian: For each given event throw ten thousand events within the parameter space defined by the uncertainty of the measured values, such as jet energy, missing Et, etc. Only events in between 90 – 300 GeV were kept. Number of events kept divided by the number of event thrown is called probability. There are two combinations of (lepton, b)’s: Pick the one having larger probability. Measuring the Top mass –

17. A function of Landau-like plus Gaussian was used to fit the templates: where pi = ai + bi  mt aiand bi are determined from the fit to templates. orig

19. The likely-hood function is defined based on fitted template function:

20. Verification: A set of six MC events were generated for different Top masses. On each set of events exercise the method to calculate the Top mass. This decides the error and the pull for each case. The error is the error from the likely-hood fit. The pull is defined as mfit morig (mfit) Measuring the Top mass- Continue -

23. First Top mass from Run II CDF preliminary, 108 pb-1 CDF preliminary, 126 pb-1 Data, 22 events 6 events Lepton+jets events with b-tagged jet Dilepton events

24. Top mass measurement- systematic error -

27. People: Jaroslav Antos, Andy Beretvas, Yen-Chu Chen, Miroslav Siket, Roman Lysak, Gong Ping Yeh, Marian Zvada Artificial Neural Network is not a ‘black’ magic! It is a non-linear probability function with its parameters optimized in the ‘training’. Using the artificial Neural Network we could improve the efficiency of identifying the Top quark with similar S/B ratio to those obtained in the traditional way. Top di-lepton event identification using Artificial Neural Network

29. Structure of the Top ANN • Input unit: 9 • The di-lepton mass • The number of jet • The Et of the most energetic jet • The Et of the second energetic jet • The missing Et • The hadron sphericity • The Qt = Pt1 + Pt2 + Et1 + Et2 + MEt • The dphi minimum • The dphi angle in between the two leptons

30. Number of unit in the hidden layer: 13 Number of unit in the output layer: 3 t : indicates Top event w : indicate WW event z : indicate Z -> ee event Structure of the Top ANN

31. Using ANN in the Top analysis - Event Pre-selection - • Criteria (2002 Winter conference based): • Met > 15 GeV • Number of leptons  2 • Number of jets  2 • Number of MC events left: • Top : 417 out of 15938 events (mt= 170 GeV) • W : 1681 out of 33101 events • Z : 491 out of 62613events

32. Using ANN in the Top analysis - ANN training - • 200 events from Top, 200 events from W and 200 events from Z were used for the ANN training. • Start to converge at the beginning. Choose to stop the training after 1000 epochs.

33. Using ANN in the Top analysis - output and error - • Error is the difference of the output and the expected value. • SSE is the sum of the square of errors in all output units. • In case of training the expected output values are: • Top : (1, 0, 0) • W : (0, 1, 0) • Z : (0, 0, 1)

34. Using ANN in the Top analysis - testing and identification - • All events after the pre-selection were used in the testing. • Using (1, 0, 0) for every events as the expected values of the output units respectively.

35. Top sample

37. W sample

39. Z sample

41. Event selection • SSE  0.4 (Good quality) • t  0.8 (Is Top) • w  0.4 (Not W) • z  0.2 (Not Z)

43. Missing Et before/after ANN

44. Had. sphericity before/after ANN

45. Di-lepton mass before/after ANN Request: # of lepton = 2

46. Efficiency • Efficiency: • MC(t ~t inclusive)generated: 10000 • HEPG filter: 5306 • events used: 15938 • events input to Top ANN: 417 • events identified: 281 • Efficiency of ANN = 281/417 = 67.4% • acceptance • =(5306/10000)*(281/15938) = 0.935 %

47. S/B ratio • t_sig = (5.4*(5306/10000)*(281/15938)) = 0.0505 • w_sig = (7.84*(393/9000)*(281/33101)) = 0.00291 • z_sig = (236*(2876/4800)*(6/62613)) = 0.0136 • S/B = t_sig/(z_sig+w_sig) = 3.06

48. Top ANN Conclusion • In this study acceptance of 0.935% in identifying Top (inclusive, 170 GeV/c2) events is reached. • The S/B(W + Z) ratio is 3.06. • More efficient with a bit more background! • Result is very preliminary!

49. Future works on Top ANN • To improve: • Jet energy correction has impact on the missing Et distribution. Proper jet energy correction means • better Top identification • better background rejection • Other variables. • Looser criteria for leptons and jets. • Larger acceptance as is done for the 2003 Summer Conference. • More background types. • Try cases of different Top mass. • Try real data: • Calculate the Top mass from the events selected by Top ANN. • Understand the background under the peak. • Cross section measurement.

50. Triggered by the sin(21) measurement in the Bd  + K, one might expect that something interesting might happen in the following decay mode: b   +  If observed one can measure the branching ratio. If enough events one could measure the alpha parameter of this decay. CP violation study could be done by comparing the alpha parameters of the b and b . An idea _