A Measurement Of WZ/ZZ ll+jj Cross Section. Rahmi Unalan Michigan State University Thesis Defense

Transcription

1 A Measurement Of WZ/ZZ ll+jj Cross Section Rahmi Unalan Michigan State University Thesis Defense 1

2 Overview Physics Motivation DØ Detector Standard Model Triple Gauge Coupling Object Definitions WZ/ZZ Signal and Backgrounds Signal Selection Cross Section Measurement 2

3 Standard Model Attempts to feed human curiosity Describes 75% of the interactions Strong force Weak force Electromagnetic force Confirmed by experiments What is the matter, how does it work? W,Z masses Existence of top quark One last piece Higgs boson Gravity is not included ν's have masses Particles gain mass through their interaction with Higgs field Too weak to In SM they are massless No Higgs yet Dark matter Only 5% of matter is SM particles. 3

4 WZ and ZZ Production An important production mechanism; provides Opportunity to probe boson self couplings Opportunity to see new physics Different coupling constants, cross sections than expected by the SM Background for Higgs particle 4

5 WZ and ZZ Production WZ Interference between diagrams results in finite cross section σ = 3.68 pb ZZ No boson self coupling σ = 1.46 pb 5

6 Accelerator Overview Protons and anti-protons collide with s = 1.96 TeV 6

7 Data Sample This research uses 1 fb-1 DØ data,runiia dataset collected during After quality checks, detector dead time etc. 7

8 proton momentum is in +z direction y PT z x 8

9 DØ Detector Muon system Excellent coverage drift chambers & scintillators Calorimetry Full coverage pre showers U/L Ar calorimeter Tracking Silicon Vertex Detector fiber tracker 2T magnetic field Significant changes were made to DØ during 2006 shutdown 9

10 Trigger System Keeping every interaction requires too large infrastructure Average event size 200 KB DØ can handle 10 MB/sec (nowadays 30 MB/sec) Selection of the most interesting events has 3 levels L1 (hardware): search for simple signatures in sub detectors (CFT, calorimeter, muon) L2 (hardware & software): from L1 info roughly define objects L3 (software): reconstruct physics objects from full readout 10

11 Jet definition: Energy deposit in calorimeter with: Must be in fiducial region ηjet<2.5 R= 2 2 Must be confirmed by L1 trigger Must have CH Fraction ( /[ + + ]) fch<0.4 EM Fraction ( / [ + + ]) 0.05<fEM<0.95 EM FINE COARSE Object Definitions: Jet pt>15 GeV after Jet Energy Scale (JES) corrections ΔR = 0.5 size cones are used to reconstruct jets 11

12 Object Definitions: Electron Electron definition: Energy deposit in calorimeter with: Must be in fiducial region EM Fraction ( +[ + ]), fem>0.90 Isolation ( / ), fiso<0.20 Gap between central and end cap calorimeters are excluded, 1.1< η <1.5 R=0.4 Must have a matched track from central tracking system Tight cut on multivariate electron discriminator, likelihoodem> 0.2 This discriminator includes track quality. shower shape(h-matrix), isolation, ET/pT R=

13 Object Definitions: Muon Muon definition: Muon system activity on both sides of toroid Must have matched wire and scintillator hits on each side of toroid Veto cosmic muons with a scintillator timing cut Single layer hit acceptable where support structure limits coverage Match to track from central tracking Central tracks have better pt resolution Suppresses fakes from calorimeter punch through into first muon layer Usage of SMT and/or beam spot information Veto cosmic muons with track DCA cut Tight isolation required Veto on high sum of other track pt in cone around muon Veto on too much calorimeter E in hollow cone T around muon 13

14 Object Definitions: Neutrino Neutrinos will not directly appear in DØ detector They can be found using other high pt particles The vector sum of all pt is zero in an event By adding all the energies in the calorimeter we can determine the imbalance in radial direction; therefore Missing ET (MET) We cannot get information in z direction since we don't know total E in the parton interaction Since calorimeter is used, we must carefully correct for the presence of muons 14

15 WZ / ZZ Event Signature Two high pt leptons Two high pt jets Almost none MET pt boost of the WZ / ZZ system is low due to high mass 15

16 ee and μμ Preselection Two charged lepton (e or μ) in the event Veto on a third, different kind of lepton At least one high pt jet pt > 15 GeV for both of the leptons in the event pt > 20 GeV Tracks must have a z-position within 2 cm of primary vertex Either leptons should fire single lepton or lepton+jets trigger or jet should fire lepton+jets trigger Veto events having jets within certain regions of ICR 16

17 ee and μμ Preselection e decay mode of Z Z mass for 1 jet inclusive μ decay mode of Z Z mass for 1 jet inclusive 17

18 MC Samples Except data and `jet background' all backgrounds and signals are generated using Monte Carlo simulations Two main programs used ALPGEN and Pythia They are further processed by DØ-GEANT to incorporate detector effects This output has to be corrected for Muon resolution Jet resolution, smearing, shifting and removing Selection efficiencies (electron, muon, jet) Trigger efficiencies Beam position Z pt distribution Samples are weighted with respect to their luminosities and theoretical cross sections to get seamless distributions 18

19 MC Samples For analysis having jet final states two more corrections ηjfirst and ηjsecond ΔR between the jets It is found that Pythia is prone to make errors in these distributions Only affects Z+jets distributions since they are hadronized through Pythia Its corrections is done only to Z+jets MC and by means of an assigned event weight 19

20 MC Samples 20

21 Backgrounds jet Backgrounds (QCD) tt W+bW-b l+νjl-νj suppressed due to faking high quality leptons Due to presence of two neutrinos, tends to have high MET Z+jet For the region we are interested, acts exactly like signal therefore we use multivariate techniques 21

22 jet Background (QCD) Z samples are clean QCD affects less Z samples Its shape is taken from the low Z mass region Then it is extrapolated to Z mass region ee QCD μμ signal QCD 22

23 jet Background (QCD) Below is the histograms for the difference in the number of events in QCD high region. Extrapolating these to an exponential gives that the number of events in the Z mass region is less than 0.5% for both channels This background is ignored in both of the channels 23

24 top - anti-top Background top anti-top signature includes very high MET A moderate MET cut kills most of the background channel cut (GeV) reduction (%) ee µµ

25 Z+jets Background Its signature is the same as WZ / ZZ signal signature Except for di-jet mass spectrum pt spectrum of the jets Angular position of the jets with respect to each other This background cannot be handled by means of simple cuts We change our way of cut application We use likelihood method to discriminate signal from this background 25

26 Pre-Selection Cuts Object identifications MZ 2 electron pt > 15 GeV ( type loose_trk) 2 muon pt > 15 GeV ( type medium) 1 jet pt > 20 GeV ( good, L1 confirmed) [70 GeV, 110 GeV ] for electron channel [50 GeV, 130 GeV ] for muon channel MET < 45 GeV for electron channel < 55 GeV for muon channel 26

27 Technique After pre-selection cuts, put analysis specific cuts to get purer sample di-jet mass, ηj1-ηj2, ptj1-ptj2 cuts Use likelihood discriminant method to separate signal from background Likelihood discriminant is defined as Mj1j2 p T_j1 P x = P x i, where x i is pt_j2 i Δp T_WZ p T_CM P sig x L x =, P sig x P back x αr rapidity Z cos(α ) j1j2 cos(α ) j1z p T_W We discovered that not every variable has the same power in every where, and this is true for cuts also. 27

28 Technique Dijet Mass Cut Example 28

29 Technique η-η Cut Example 29

30 Technique pt-pt Cut Example 30

31 Technique Regions and Cuts The phase space is divided into 4 regions with respect to ΔR distance between the two jets Applying a cut blindly with an assumption that it has the same power of reducing background everywhere is wrong. Cuts are optimized in these four regions by = Region ΔR M η-η η-η p-p p-p N signal N signal N background I < 1.8 II III IV chan 1.8, < , < ee,μμ [50,130] ee,μμ > > > > ee > > > > μμ (74,53) (73,42) (78,34) (49,46) ee (201,39) (114,29) (84,24) (33,52) μμ 31

32 Technique Likelihood Reminder M P sig x j1j2 L x =, P sig x P back x p T_j1 P sig x = P sig x i, i P bkg x = P bkg x i i Mjj : Dijet Mass ptj1 : First Jet pt ptj2 : Second Jet pt ΔpTWZ : W,Z pt difference ptcm : W,Z CM frame pt pt_j2 Δp T_WZ p T_CM αr rapidity Z cos(α ) j1j2 cos(α ) j1z p T_W αr : Angle btw Z and CM ΥZ : Z rapidity cos αj1j2 : cos of angle j1 j2 cos αj1z : cos of angle j1 Z ptw : W pt 32

33 Technique Likelihood Examples 33

34 Technique Likelihood Examples 34

35 Technique Likelihood Examples 35

36 Technique Likelihood Examples 36

37 Technique Likelihood Examples 37

38 Likelihood 38

39 Likelihood From the figures it can be inferred that the Monte Carlo events used can describe data well Then likelihood discriminant of the data can be also described by MC By using following formula we obtained a fit to data D= f S 1 f B 39

40 Cross Section Individual channels cross section results: ee : σwz / ZZ = 3.03 ± 0.65 pb μμ : σwz / ZZ = 4.21 ± 0.83 pb 40

41 Technique Likelihood Examples 41