Search for mSUGRA signature at D Ø in

1. Search for mSUGRA signature at DØ in John Zhou Iowa State University Ph.D. Thesis Defence

2. Outline Broken Symmetry • Introduction to myself • Introduction to this analysis • Data Selection • Event Simulation • Background Estimation • Signal Analysis • Results • Conclusion

3. Introduction to myself

4. What’s the Point? High Energy Particle Physics studies the smallest pieces of matter the elementary particles. It investigates (among other things) the nature of the universe immediately after the Big Bang, i.e., what the universe is made of, how the particles interact, and why they interact the way as is. It’s a lot of fun and pain.

5. u d u u d d Proton Neutron Standard Model: a quantum theory that includes theory of strong interactions (Quantum Chromodynamics, QCD) and the unified theory of weak and electromagnetic interactions (electroweak theory). It is well tested experimentally to a very high precision.

6. The Standard Model Standard Model A quantum theory that includes theory of strong interactions (Quantum Chromodynamics, QCD) and the unified theory of weak and electromagnetic interactions (electroweak theory). It is well tested experimentally to a very high precision.

7. Limitation of the Standard Model • There are minimal 18 parameters in the SM that have to be put in hand (highly undesirable for an “ultimate” theory. • Electroweak Symmetry Breaking (EWSB) through an ad hoc way: The Higgs Mechanism. It is responsible for generating mass for SM particles. • The hierarchy problem: Higgs potential: Higgs mass: EW scale: If SM valid to GUT scale: f s H H

8. Supersymmetry Supersymmetry incorporates additional symmetry between fermions and bosons. For each SM particle, there is a SUSY partner with spin differ by 1/2.

9. Why supersymmetry? • Solves the fine tuning problem by exact cancellation of each fermion loop with a scalar loop. • Higgs potential turns negative at EW scale by evolving the SUSY parameters down from GUT scale, naturally gives EWSB. • GUT candidate Main motivation.

10. Introduction to mSUGRA Supersymmetry breaking origin (Hidden sector) MSSM (Visible sector) Gravity Parameters of mSUGRA • m0 - common scalar mass at MSUSY • m1/2 - common fermion mass at MSUSY • A0 - common soft trilinear coupling • tan(β) - ratio of the Higgs vacuum expectation values • sign(μ) - μ is a Higgsino mass parameter R-parity: +1 for SM particles, -1 for SUSY particles. R-parity is assumed to be conserved in this analysis.

11. Enrico Fermi

12. The Dzero Detector

13. Dzero Coordinate

14. A “single” muon tt event

15. Current mSUGRA limit ALEPH Limit DØ Dilepton Limit

16. This analysis: Search for mSUGRA in single electron channel Motivation: • Search for parameter space which is sensitive chargino and neutralino decays to W and Z respectively large jet multiplicity (not as sensitive in other channels). • Complements other mSUGRA search channels at DØ. Signal: electron +  4 jets + ET is the LSP.

17. Event selection DØ 1994-95 data ELE_JET_HIGH(A) trigger • Luminosity = 92.7 pb-1 • 1 isolated electron [elike<0.5(0.3) in CC(EC), fiso<0.1] • GeV, • or • 4 jets [cone size = 0.5, pass EM and CH fraction cuts] • GeV, • Missing Energy • ET > 25 GeV • No second loose electron[elike<1.0, fiso<0.3(dilepton signal electron definition)] • No good muon with • We observed 72 events.

18. Backgrounds • Physics • Standard Model e +  4 jets + ET • W +  4 jets • t t • WW +2 jets • Instrumentation • QCD 5 or more jets with one jet faking an electron and inaccurately measured jet energies leading to ET

19. Event simulation: Fast MC, to explore the large parameter space Kinematic cuts Signal, ttbar, and WW bkgd are simulated with FMCØ. Structure of FMCØ: Physics Event Generator (PHTHIA) Kinematic Ntuple elecs jets muons ET s_elecs s_jets s_muons s_ET Event Weighting (trigger) Event Weighting (object ID) particles Jet (RECO) Object Smear acceptance

20. Data, GEANT, and FMCØ comparison Total acceptance of t t: Total trigger efficiencies: Conclusion: These and many other checks have concluded that FMCØ models the detector and the trigger system very well. It has also indicated that the ID efficiencies are accurately measured.

21. QCD background CC EC Fake electron ET distributions are normalized to the good electron ET distribution in the low ET region (dominant by QCD background). Tails in the fake sample in the high ET region (the signal region is ET > 25 GeV) models the QCD background in the signal region. Result: 19.1  4.7 events

22. W + 4 jet background • We do not rely on Monte Carlo cross section calculation because it suffers large high order correction. Yet event can be generated for kinematic and detector acceptance calculation. • We do not use data exclusively to calculate the W+ 4 jet background because of the small amount of available data events. • The method to calculate W+ 4 jet background uses both MC and data. The method is based on the assumption that the number of W+ jet events should follow the power law: scaled by s. • is measured from data with the aid of MC (smaller stat. err.). • is measured for different jet multiplicity and is inferred. • is measured using FMCØ.

23. Measuring Method: • from Monte Carlo and Neural Network, find a kinematic region where the W+3 jet events dominate; • keep the total number of W+3 jet events afloat and match its spectrum + background spectrum to the data in that kinematic region (defined in terms of NN output); • from the matching we estimate .

24. Introduction to Neural Network • Neural Network mimics human to do pattern recognition. • For a feedforward NN with one hidden layer (multilayer perceptron, MLP) and one output node, the output represents the Bayesian posterior probability of signal. • Pattern recognition in HEP usually means to distinguish signal from background. • Signal and background distribute differently in a multi-variable space. A function (usually non-linear) exists to map the variables to a binary output: is the vector of input variables. • NN tries to approximate this mapping function through linear and non-linear transformation of the input variables. Mathematically, it has been proven that MLP can approximate any mapping function to arbitrary accuracy if it has enough hidden nodes. Sigmoid Function (non-linear)

25. Measuring using NN NN Variables: • ; • ; • ; • ; • ; • ; • ; • Aplanarity. NN structure: X-2X-1 Data and MC matching: NN output0.5 - 1.0 Result:

26. Measuring the scaling factor • measure for i = 1, 2, 3, and 4 inclusive jet multiplicity ( is the number of W+i jet events measured from a sample with minimal jet trigger bias); • fit to obtain . NN training Data/MC matching

27. Measuring 

28. Total number of background events • t t : 17.4  5.5 • PYTHIA generator + FMCØ • WW +  2 jets : 1.4  0.3 • PYTHIA generator + FMCØ • QCD: 19.1  4.7 • Estimated from data • W +  4 jets : 32.2  5.7 • Estimated from data and MC • Total background: 70.1  9.2 Data agrees with the Standard Model background very well! But wait … Observed: 72 events

29. Data-Model Comparison

30. NN variables to separate mSUGRA signal from background Variables: • ; • ; • ; • ; • ; • Aplanarity; • ; • . tt 3C fit

31. Conclusions of Data-Model Comparison Conclusions: • The observed data are well explained by the Standard Model backgrounds. • The existence of signal is not conclusive based on the observed number of data events and the estimated number of background events and error. • We proceed to set limit on the signal. • Remember these 72 events survived only the initial cuts. More optimized cuts can enhance the signal sensitivity. We use NN to do this.

32. tt 3C fit

33. of signal and t t

34. NN training result Signal: m0=170 GeV m1/2=60 GeV tan() = 3  < 0 A0 = 0  = 31.5 pb (SPYTHIA) a = 0.0056 (FMCØ) Nevents = 16.3  2.9

35. NN cuts at where the significance is maximal. For this particular param. set: NN cut = 0.80 Nsignal=9.51.7, Nbkgd=4.5±0.9 Significance and NN cut The expected significance: where and S(n|b) the number of standard deviation that background must fluctuate to at least n events:

36. 95% confidence cross section upper limit: where is the probability of signal production cross section given k observed events. If the model is excluded. Result

37. Conclusions of mSUGRA search • We search for mSUGRA with D Run I data in the electron +  4 jets + channel. • 72 events are observed with 70.1  9.2 expected SM background events. • No signal is observed in our data. • We use Neural Network to enhance signal sensitivity. • We obtain an improved limit from dilepon analysis in moderate m0 region.

38. We can turn the problem around to measure . Same QCD and WW background as in the mSUGRA search. Same method in measuring the W+  4 jet background, except no mSUGRA signal involved in NN training; is used as a parameter in obtaining and . New measurement of • WW +  2 jets : 1.4  0.3 • PYTHIA generator + FMCØ • QCD: 19.1  4.7 • Estimated from data • W +  4 jets : 36.4  7.3 • Estimated from data and MC • Total background: 56.9  8.7 Observed: 72 events.

39. NN variables to separate tt signal from background Variables: • ; • ; • ; • ; • ; • Aplanarity; • ; • 2of 3C fit . tt 3C fit

40. Result • More optimized kinematic cut (8 variables vs. 2 variables); • Better consistency (expect 72-56.9=15.1 vs. 39-35.5=3.5 t t events before optimization); • More observed candidates with similar background rate: 19 observed with 4.550.76 expected background events vs. 9 observed with 4.510.91 expected background events.

41. Grand Finale Thank you all for being on my committee! Would you grant me the Ph.D.?

42. Electron ID efficiency (method) eid = 5(4)-variable electron likelihood in CC(EC) and fiso • Di-EM data sample • Clean up the sample with a relative tight tag electron • Plot M(ee) of the probe and tag electrons • Subtract background (mostly Drell-Yan) using the side-band method • Apply eid cuts and do the above steps again

43. Electron ID efficiency (results)

44. Jet ID cuts (definition) • Universal ID cuts: • Additional CC cuts: • Additional EC cuts: • emf should be moderate for quark or gluon jets. It is large for electron and small for hadronic noise. • Large chf usually associates with noise from main-ring beam loss.

45. Jet ID efficiency (derivation) N: number of entries between the cuts Nlow: area under the curve below the lower cut Nhigh: area under the curve above the upper cut

46. Jet ID efficiency (results) Jet ID efficiencies are parameterized as:

47. ELE_JET_HIGH(A) trigger • Level 1: • 1 EM tower: • 2 jet towers: • Level 2: • 1 electron: • 2 jets (0.3 cone): • Missing ET:

48. Two assumptions: Shape of turn-on curve does not change over a few GeV Offline electron ET scales as trigger level ET Method: Obtain a turn-on curve of a hypothetical EM trigger threshold T2=qT1, where T1 is the trigger of interest and q>1. Scale the offline electron ET by 1/q. Electron trigger efficiency (method) Level 1 electron trigger efficiency

49. Level 2 Electron trigger efficiency

50. Single jet trigger efficiency • Single jet trigger efficiency is needed to calculate the trigger efficiency for events with multi-jets. • Method: • Use EM1_EISTRKCC_MS trigger which has minimum trigger bias • Require one jet and a full trigger efficiency on the EM part • Count the number of events passing ELE_JET_HIGH as a function of jet ET