1. Preliminary Exam April 26 th, 2005HWIDONG YOOSlide 1 Luminosity Monitor & Top Analysis at DØ Hwidong Yoo Brown University Preliminary Examination, April 26 th, 2005

2. Slide 2 Outline Tevatron & DØ Detector Overview X section & Luminosity Luminosity Monitor(LM) Luminosity Measurement LM Readout System Calibration for Readout Cards Top Quark in the SM Top Production & Decay Mode B-tagging Neural Network (NN) Variables for NN Plan

3. Slide 3 Tevatron Overview Main Injector Tevatron p p CDF D0 Proton – Antiproton collisions at E cm = 1.96TeV

4. Slide 4 DØ Detector Overview Silicon Microtrip Tracker (SMT) Central Fiber Tracker (CFT) Central & Forward Preshower Calorimeter Muon Detector Luminosity Monitor

5. Slide 5 What does an interaction look like? Beams traveling into and out of the screen Tracker finds many charged particle tracks Calorimeter energy represented by “lego blocks” Muon detector (not shown) finds two tracks: the green color

6. Slide 6 Cross Section Total cross section = σ pp tot = 79.1 ± 4.0mb (√s = 1.96 TeV) Elastic: σ pp tot - σ pp inelastic Inelastic: σ pp inelastic = 60.7 ± 2.4mb (√s = 1.96 TeV) single diffractive dissociation: σ SD = 9.6 ± 0.5mb Double-diffractive dissociaton: σ DD = 7.2 ± 2.0mb non-diffractive or hard-core scattering: σ HC = σ pp − σ SD − σ DD ⇒ High Pt Physics ⇒ We are interested in only Inelastic Collison : Most of interesting productions are coming from inelastic collisions Measured by E811, CDF and E710

7. Slide 7 What is Luminosity? Luminosity is the product of incident particle flux (particles per unit time) with particles per unit area (cm -2 s -1 ) How can we measure? ⇒ Determine the luminosity by measuring the count rate of inelastic p-pbar collisions Where do we use? : to normalize DØ analyses ⇒ Calculate the production cross sections LM efficiency: extracted from data using forward calorimeter LM acceptance: determined from MC Inelastic pp cross-section: measured by other experiments σ pp, eff = 46 ± 3mb (6.5% error) at DØ Xsection unit: 1barn = 10 -24 cm 2

8. Slide 8 Integrated Luminosity

9. Slide 9 Luminosity Monitor (LM) Plastic Scintillators + PMTs North & South Arrays Mounted in front of Calorimeter Endcaps LM  Purpose 1.Measure the Luminosity 2.Position of the interaction vertex Z vtx = c/2(T N – T S ) FASTZ : |Z vtx | < 97cm PHalo: -166cm < Z vtx < -116cm AHalo: 116cm < Z vtx < 166cm 3.Produce minbias trigger PMT Scintillator

10. Slide 10 Luminosity Measurement North South ct = 0 ct = L/2 ct = 0 ct = L/2 ct = L time diff ~0 -135cm +135cm 2.7 < |η| < 4.4 Z = 0 ct = 0 ct = L NorthSouth North South time diff ~ L/c So, we can distinguish between FastZ and Halos pp

11. Slide 11 LM Readout(1) Readout Crates Configuration 1 PowerPC 1 SBC 1 MFC 6 TDCs (3 North, 3 South) Digitizing and processing PMT signals Calculating Quantities: Corrected time, # of Hits, Sum of times, Largest times, Smallest times, … 1 VTX Calculating Quantities: Average times, Time difference, Z vertex, … Providing output to Luminosity Scalar and Trigger

12. Slide 12 LM Readout(2) Lum counting We need calibration for this!

13. Slide 13 Calibration for Readout(1) By measuring the Average ADC output of a CAFÉ card as a function of the VCAL DAC setting, the CAFÉ cards may be calibrated! I’m contributing to take these data…

14. Slide 14 Calibration for Readout(2) Histogramming Main Controller Pop-up

15. Slide 15 Top Quark in SM

16. Slide 16 Top Pair Production ~ 85% g g ~ 15% t → Wb ≈ 100 %

17. Slide 17 Top Decay Modes All Jets( 36/81 ): large fraction but too much background Di-lepton( 9/81 ): Enable to reduce the background but small fraction Lepton + Jets ( 36/81 ): large fraction and not much background We’re trying the Lepton- Jets Mode (especially, e+jets, mu+jets)

18. Slide 18 B-tagging(1) Motivation At least 4 jets are in top pair production event, including 2 b-quark jets in lepton-jets mode If we identify b-jets, we can know which jets are from W bosons and it makes it easier to calculate the properties of the top quark ex) top mass, R(t → Wb/t → Wq) … b Properties b quark has a large mass ⇒ M b ≈ 4.25 GeV/c 2 B hadrons have τ ≈ 1.56 ps ⇒ decay length ≈ 500μm(short, but not too short) ⇒ Secondary Vertex Hard fragmentation ⇒ particles coming from B have large P t Semi-leptonic decay of B ⇒ BR Γ(B → μX) ≈ 10%

19. Slide 19 B-tagging(2) Secondary Vertex Tag (SVT): This Method makes use of the long life time of B- hadrons by searching for a secondary vertex, well separated from the Primary vertex Taggability: if ΔR < 0.5 to a track-jet, it’s taggable Decay Length Significance = L / σ(L) ΔR = sqrt(Δη 2 + Δφ 2 ) ε : ~60% Single-tagging Eff ≈ ε(1- ε) + (1-ε)ε + ε 2 ≈ ~80% Double-tagging Eff ≈ ε 2 ≈ ~30% We want higher efficiency for Double-tagging!!!

20. Slide 20 What is Neural Network?(1) What is our plan for higher double-tagging efficiency? Use kinematics of B hadron decays Require at least one secondary vertex Include lepton-tagging What problem am I trying to solve? : The shapes of b-jets and l-jets for kinematic variables are not much different ⇒ hard to use cuts How can NN be used to solve this problem? NN can be trained to recognize detailed features of the signal and background, including the correlations between variables So, NN may provide the optimized separation of signal from background sig bg

21. Slide 21 What is Neural Network?(2) Use MLP package in ROOT Connect and weight the input variables in 8 hidden nodes Output is an estimator between 0(bg) and 1(sig) Train the NN with 5000 ttbar MC events I1I1 I2I2 h1h1 h2h2 h3h3 Out Input Nodes Hidden Nodes Output Nodes weight 1 weight 2 In our case: - 8 input nodes - 8 hidden nodes - 1 output node Event Requirement MC ttbar production(signal) P.V. cut: Z < 60 cm, # of tracks ≥ 3 Jet Selection: jet Pt > 15GeV, Eta ≤ 2.5

22. Slide 22 NN Variables(1) mass jet = sqrt(E jet 2 –P jet 2) ) E jet pT jet

23. Slide 23 NN Variables(2) pT track E Σtrack Mass track = √( (Σ|P trk |) 2 – (Σ vecP trk ) 2 )

24. Slide 24 NN Variables(3) Jet axis Track ΔR < 0.5 relative P t P.V. Multiplicity track The Relative P t

25. Slide 25 NN Results It’s not well separated yet, but we’re still trying to make it better!!

26. Slide 26 Plan Find more good NN variables Add Lepton-tags & Sec. Vertex Info. Optimize the training and the output Apply the double b-tagging to the Lepton-Jet mode Test with the other b-tagging methods ( CSIP, JLIP ) Apply to Top Physics Analysis