1. LHCb: Preparing for Data (A talk on MC events and data expectations) NIKHEF Colloquium Feb 4, 2005 Marcel Merk

2. 2 Contents  Last year: Several excellent overviews of latest B physics results An overview of the status of the LHCb detector  This talk: What does LHCb plan to do with incoming data in ~ 2008? Illustrate with a single decay mode: B s →D s h  Topics: B s →D s  & B s →D s K Detector Simulation Reconstruction and Trigger Event Selection and Flavour Tagging Physics Sensitivity studies

3. 3 The Decay B s →D s h  Two decays with identical topology: B s → D s -   B s -> D s ∓ K ± btbt BsBs KK KK  ,K   DsDs Primary vertex  Experiment : Trigger on B decay of interest. Signatures: “high” Pt tracks displaced vertices pp Select the B decay and reject the background Tag the flavour of the B decay Plot the tagged decay rate as function of the decay time  Physics of these two decays however is different….

4. 4  Dilutions: A(t) : Trigger acceptance W tag : Flavour Tagging  t : Decay time Resolution  Fit them together with  m Physics with B s - →D s -  + :  m b s c s d u BsBs Ds-Ds- ++ BR~10 -4 1 year data LHCb Measure Oscillation Frequency!  In the fitting procedure we use the individual decay rates

5. 5 Physics with B s →D s ∓ K ± :  b s c s s u BsBs Ds-Ds- K+K+ BsBs s b b s Ds-Ds- b s u s s c BsBs K+K+ + BR~10 -5 V ub  Introduce also:  = strong phase difference ; r = ratio between amplitudes

6. 6 Physics with B s →D s ∓ K ± :   2 asymmetries to fit the unknown parameters: Ration between diagrams: r Strong phase:  Weak phase  b s c s s u BsBs Ds-Ds- K+K+ BsBs s b b s Ds-Ds- b s u s s c BsBs K+K+ + BR~10 -5 Measure Oscillation Amplitude!  4 decay rates to fit the unknown parameters: Ration between diagrams: r Strong phase:  Weak phase    Same experimental dilutions as in D s  should be added: Use the value of A, w tag and  t as obtained with D s  fit… B s → D s - K + B s → D s + K -

7. 7 B Production @ LHC Forward (and backward) production  Build a forward spectrometer bb bb O(50%) O(10%) O(40%) Pythia & hep-ph/0005110 (Sjöstrand et al)

8. 8 LHCb detector: a quick reminder  pp ~ 200 mrad ~ 300 mrad (horizontal) 10 mrad  Inner acceptance ~ 15 mrad (10 mrad conical beryllium beampipe)

9. 9 LHCb tracking: vertex region   VELO: resolve  m s oscillations in e.g. D s  events

10. 10 Pile-Up Stations Interaction Region  =5.3 cm LHCb tracking: vertex region  y x y x

11. 11 LHCb tracking: momentum measurement  B y [T] Total Bdl = 4 Tm Bdl Velo-TT=0.15 Tm  Tracking: Mass resolution for background suppression in eg. D s K

12. 12 LHCb tracking: momentum measurement  All tracking stations have four layers: 0,-5,+5,0 degree stereo angles. ~6  5 m 2 ~1.4  1.2 m 2

13. 13 LHCb Hadron Identification: RICH  3 radiators to cover full momentum range:  Aerogel  C 4 F 10 CF 4   RICH2 100 m3 CF4 n=1.0005  RICH: K/  separation e.g. to distinguish D s  and D s K events.  RICH1 5 cm aerogel n=1.03 4 m 3 C 4 F 10 n=1.0014

14. 14 LHCb calorimeters  e h  Calorimeter system to identify electrons, hadrons and neutrals and used in the L0 trigger: hadron P t trigger for D s h events

15. 15 LHCb muon detection    Muon system to identify muons and used in L0 trigger e.g. unbiased trigger on “other B” for D s  events

16. 16 Simulation Software: “Gaudi” Applications  Event Generator: Pythia: Final state generation Evtgen: B decays  Detector Simulation: Gauss: GEANT4 tracking MC particles through the detector and storing MC Hits  Detector Response (“digitization”): Boole: Converting the MC Hits into a raw buffer emulating the real data format  Reconstruction: Brunel: Reconstructing the tracks from the raw buffer.  Physics: DaVinci: Reconstruction of B decays and flavour tags. LoKi : “Loops and Kinematics” toolkit.  Visualization: Panoramix: Visualization of detector geometry and data objects

17. 17 Event Generation: Pythia  Pythia 6.2: proton-proton interactions at √s = 14 TeV. Minimum bias includes hard QCD processes, single and double diffractive events  inel = 79.2 mb bb events obtained from minimum bias events with b or b- hadron  bb = 633  b  Use parton-parton interaction “Model 3”, with continuous turn-off of the cross section at P T min. The value of P T min depends on the choice of Parton Density Function. Energy dependence, with “CTEQ4L” at 14 TeV: P T min =3.47 ± 0.17 GeV/c. Gives:  Describes well direct fit of multiplicity data:  Robustness tests…

18. 18 Charged multiplicity distributions at generator level In LHCb acceptance ( 1.8 <  < 4.9 ) Average charged multiplicityMinimum biasb CDF tuning at 14 TeV 16.53 ± 0.0227.12 ± 0.03 LHCb tuning, default p T min 21.33 ± 0.0233.91 ± 0.03 LHCb tuning, 3  low p T min 25.46 ± 0.0342.86 ± 0.03

19. 19 The LHC environment  pp collisions @  s=14 TeV  Bunch crossing @ 40MHz 25 ns separation   inelastic = 80mb At high L >>1 collision/crossing  Prefer single interaction events Easier to analyze! Trigger Flavor tagging  Prefer L ~ 2 x 10 32 cm -2 s -1 Simulate 10 hour lifetime,7 hour fill  Beams are defocused locally Maintain optimal luminosity even when Atlas & CMS run at 10 34

20. 20 Simulation: Switched from GEANT3… VELO RICH1 TT T1 T2 T3

21. 21 …to GEANT4 (“Gauss”) Note: simulation and reconstruction use identical geometry description.

22. 22 Event example: detector hits

23. 23 Event example (Vertex region zoom)

24. 24 Detector Response Simulation: e.g.: the Outer Tracker Geant event display OT double layer cross section 5mm straws pitch 5.25 mm Track e-e- e-e- e-e- e-e- e-e- 1 bunch + Spill-over + Electronics + T0 calibration TDC spec.:

25. 25 Track finding strategy VELO seeds Long track (forward) Long track (matched) T seeds Upstream track Downstream track T track VELO track T tracks  useful for RICH2 pattern recognition Long tracks  highest quality for physics (good IP & p resolution) Downstream tracks  needed for efficient K S finding (good p resolution) Upstream tracks  lower p, worse p resolution, but useful for RICH1 pattern recognition VELO tracks  useful for primary vertex reconstruction (good IP resolution)

26. 26 Result of track finding Typical event display: Red = measurements (hits) Blue = all reconstructed tracks Efficiency vs p :Ghost rate vs p T : Eff = 94% (p > 10 GeV) Ghost rate = 3% (for p T > 0.5 GeV) VELO TT T1 T2 T3 On average: 26 long tracks 11 upstream tracks 4 downstream tracks 5 T tracks 26 VELO tracks 20  50 hits assigned to a long track: 98.7% correctly assigned Ghosts: Negligible effect on b decay reconstruction

27. 27 Robustness Test: Quiet and Busy Events  Monitor efficiency and ghost rate as function of n rel : “relative number of detector hits” = 1

28. 28 Kalman Track Fit  Reconstruct tracks including multiple scattering.  Main advantage: correct covariance matrix for track parameters!! z Impact parameter pull distribution:  = 1.0 Momentum pull distribution:  = 1.2

29. 29 Experimental Resolution  p/p = 0.35% – 0.55% p spectrum B tracks  IP = 14  + 35  /p T 1/p T spectrum B tracks Momentum resolution parameter resolution Impact parameter resolution

30. 30 Particle ID RICH 1 RICH 2  (K->K) = 88%  (p->K) = 3% Example: Bs->Dsh KK  BsBs KK  ,K  DsDs Prim vtx

31. 31 Trigger 40 MHz pile-up 1 MHz 40 kHz 2 kHz output Level-1: Impact parameter Rough p T ~ 20% HLT: Final state reconstruction Calorimeter Muon system Pile-up system Vertex Locator Trigger Tracker Level 0 objects Full detector information L0 Level-0: p T of , e, h,   ln p T  ln IP/  IP L1 Signal Min. Bias B->  Bs->DsK

32. 32 Trigger Acceptance function  Impact parameter cuts lead to a decay time dependent efficiency function: “Acceptance” B s →D s K Acc

33. 33 B s →D s h Reconstruction  Final state reconstruction Combine K + K -  - into a D s - Good vertex + mass Combine D s - and “bachelor” into B s Good vertex + mass s Pointing Bs to primary vtx K/  separation Mass distribution: DsDs BsBs KK KK  ,K   d p 47  m 144  m 440  m

34. 34 Annual Yields and B/S  Efficiency Estimation:  det (%)  rec/det (%)  sel/rec (%)  trg/sel (%)  tot (%) B s →D s  5.480.625.031.10.337 B s →D s  5.482.020.629.50.269  Background Estimation: Currently assume that the only background is due to bb events Background estimates limited by available statistics DecayAnnual yieldB/S B s →D s  82k0.32 ± 0.10 B s →D s  5.4k<1.0 (90%) C.L.  Estimation of B s →D s  background in the B s →D s  sample: B/S = 0.111 ± 0.056

35. 35 Decay time reconstruction: t = m d / p B decay time resolution: Pull distribution: Error distribution Measurement errors understood! As an illustration, 1 year B s →D s -  

36. 36 Flavour tag l B0B0 B0B0 D  Ds-Ds- K-K- b b s u s u Bs0Bs0 K+K+ tagging strategy:  opposite side lepton tag ( b → l )  opposite side kaon tag ( b → c → s ) (RICH, hadron trigger)  same side kaon tag (for B s )  opposite B vertex charge tagging 43542  eff [%] Wtag [%]  tag [%] 63354 B d   B s  D s h Combining tags effective efficiency :  eff =  tag (1-2w tag ) 2 sources for wrong tags: B d -B d mixing (opposite side) b → c → l (lepton tag) conversions…  Knowledge of the B flavour at production is needed for the asymmetries

37. 37 Sensitivity Studies  Many GEANT events generated, but: How well can we measure  m s with B s →D s  events? How well can we measure angle  with B s →D s K events? as function of  m s,  s, r, , , and dilutions w tag,  t, …?  Toy MC and Fitting program: Generator: Generate Events according to theory B decay formula An event is simply a generated B decay time + a true tag. Simulator: Assign an observed time and an error Use the full MC studies to do the smearing Fitter: Create a pdf for the experimentally observed time distribution and fit the relevant parameters

38. 38 Toy Generator  Generate events according to the “master” formula for B decay Relevant physics parameters: For D s + K - : replace  by  -  For D s  : Simplify: r=0 B s →D s - K + B s →D s + K B s →D s + K - With:

39. 39 Toy Simulation  Smear theoretical events ( t=t true ) into experimental events ( t rec ) and assign an experimental error (  t rec ). Method: From the full simulation make a lookup table with selected events:  t true i, t rec i,  t rec i Generate t true in toy and assign t rec and  t rec from look-up table, such that non-Gausian effects of the full simulation are included  For  tag fraction of the events assign an event tag: Statistically assign 1 -w tag correct tags, and w tag wrong tags. Current studies  tag = 54% w tag = 33%.  Apply an acceptance function A( t rec ) by statistically accepting events according to the acceptance value for a given event time.

40. 40 Dilutions in B s →D s   Plot the MC toy decay rate with the following situation: 1 year data B s →D s -  + Experimental Situation: Ideal resolution and tag

41. 41 Dilutions in B s →D s   Plot the MC toy decay rate with the following situation: 1 year data B s →D s -  + Experimental Situation: Ideal resolution and tag Realistic tag

42. 42 Dilutions in B s →D s   Plot the MC toy decay rate with the following situation: 1 year data B s →D s -  + Experimental Situation: Ideal resolution and tag Realistic tag Realistig tag and resolution

43. 43 Dilutions in B s →D s   Plot the MC toy decay rate with the following situation: 1 year data B s →D s -  + Experimental Situation: Ideal resolution and tag Realistic tag Realistig tag and resolution Realistic tag + reso + background

44. 44 Dilutions in B s →D s   Plot the MC toy decay rate with the following situation: Experimental Situation: Ideal resolution and tag Realistic tag Realistig tag and resolution Realistic tag + reso + background Realistic tag+reso+bg+acceptance 1 year data B s →D s -  +

45. 45 The signal for D s  and D s K 5 years data: B s → D s -   B s → D s - K +  m s = 20)  The CP signal is not self-evident  Use full statistical power in the data

46. 46 Fitting time dependent decay rates  Why use complicated Likelihood fit method? Weigh precisely measured events differently from badly measured events Rely on the reconstructed event error Allow for a scale factor in the analysis Error distr Pull distr

47. 47 Likelihood Fitter (general idea)  The likelihood that nature produces an event at a given time t =  The probability that this event is reconstructed (i.e. observed) at a reconstructed time t rec with measurement error  t rec =  Thus the likelihood of observing an event ( t rec,  t rec ) =  Fit the physics parameters (  m, ,…) in R such that the likelihood is maximal:.i.e. maximize:

48. 48 Likelihood Fitter (for the die-hard) Maximize an unbinned likelihood describing the best theory curves simultaneously matching simultaneously the 4 decay rates for Bs->Ds  and 4 decay rates for Bs-> Ds K Normalization of the Likelihood is interesting! See also LHCb note…LHCb 2003-124 (Include information of the relative overall rates) (Slow computation!) Event probab: Normalization of the probability: Create the Likelihood: Fit parameters: -Physics: -Experimental: 1 year data: B s -> D s -  + B s -> D s - K +

49. 49 Strategy for D s  / D s K fits  It turns out to be difficult to fit simultaneously the wrong tag fraction, resolution and acceptance function. A small bias in the acceptance function biases the resolution fit  A possible solution could be a 4 step procedure: 1.Calibrate the experimental time resolution 2.Fit the acceptance function on the untagged sample of B s ->D s  events 3.Fit simultaneously the values of  m s, w tag with D s  events. 4.Fit the values of the r, ,  with the D s K sample

50. 50 1.Fitting the measurement errors  Resolution can be determined from the negative tail of the lifetime distribution. Fit with 10% of 1 year data: S·  t rec. => S = 0.99 ± 0.04  Can L1 trigger be tuned to provide unbiased B s -> D s  events? What would be the required bandwidth for this?  In any case unbiased samples of J/  events are foreseen. S=0.99+- 0.04 L1 trigger t rec 10% of 1 year untagged B s →D s 

51. 51 2. Fitting the acceptance function  The acceptance function is modelled as:  The function can easily be determined using the unbiased sample 1 year untagged B s →D s  t rec Acc

52. 52 3. + 4. Fit the Physics parameters  Use the 4 tagged (B) and (B) D s  decay rates to fit  m s and W tag fraction  Use the 4 tagged D s K events to fit r, ,  5 years data: B s → D s -   B s → D s - K +  m s = 20)  Actually perform the D s  and D s K fits simultaneous  For each setting of the parameters repeat ~100 toy experiments A task for the GRID

53. 53 The sensitivity of  m s after 1 year  The sensitivity for  m s Amplitude fit method analogous to LEP Curves contain 5 different assumptions for the decay time resol. 55  Sensitivity:  m s = 68 ps -1 msms 15202530 (ms)(ms) 0.0090.0110.0130.016  Precision on  m s in ps -1 ~1000 jobs

54. 54 CP Sensitivity for many parameter settings ++ 5565758595105 (+)(+) 14.514.215.0 15.1  -20-100+10+20 (+)(+) 13.914.114.214.514.6 msms 15202530 (+)(+) 12.114.216.218.3 s  s /  s 0 0.10.2 (+) (+) 12.1 14.216.2  Precision on angle  after one year with 1 year data:   10 o Dependence on backgroundDependence on resolution (Ab-)using the GRID

55. 55 (My) Conclusions  The decay B s →D s  can provide an observation of  m s oscillations in the first year of data taking. Important are: A working hadronic trigger A good tagging procedure Fairly good resolution  The decay B s →D s K can provide an observation of angle  in subsequent years. Important are: Very good mass resolution for background suppression Full understanding of time resolution and tagging for systematics An efficient K/  separation

56. 56 Outlook  A possible scenario before the LHCb measurement of 

57. 57 Outlook  A possible scenario after the LHCb measurement of 

58. 58 The End