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LHCb: Preparing for Data (A talk on MC events and data expectations)

LHCb: Preparing for Data (A talk on MC events and data expectations). NIKHEF Colloquium Feb 4, 2005 Marcel Merk. Contents. Last year: Several excellent overviews of latest B physics results An overview of the status of the LHCb detector. This talk:

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LHCb: Preparing for Data (A talk on MC events and data expectations)

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

  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: Bs→Ds h • Topics: • Bs→Dsp & Bs→DsK • Detector • Simulation • Reconstruction and Trigger • Event Selection and Flavour Tagging • Physics Sensitivity studies

  3. The Decay Bs→Ds h ,K Bs K K Ds  Primary vertex bt • Two decays with identical topology: • Bs→ Ds-p + • Bs -> Ds∓ K± p p • Experiment: • Trigger on B decay of interest. Signatures: • “high” Pt tracks • displaced vertices • 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. Physics with Bs-→Ds-p+ : Dm p+ d u b c Bs Ds- s s BR~10-4 • Dilutions: • A(t) : Trigger acceptance • Wtag: Flavour Tagging • dt: Decay time Resolution • Fit them together with Dm Measure Oscillation Frequency! 1 year data LHCb • In the fitting procedure we use the individual decay rates

  5. Physics with Bs→Ds∓ K± : g Ds- Vub K+ s s u c b c s b b u Bs Bs Ds- Bs K+ s s s s b s BR~10-5 • Introduce also: d = strong phase difference ; r = ratio between amplitudes +

  6. Physics with Bs→Ds∓ K± : g Ds- K+ s s u c b c s b b u Bs Bs Ds- Bs K+ s s s s b s BR~10-5 • 2 asymmetries to fit the unknown parameters: • Ration between diagrams: r • Strong phase: d • Weak phase: g + Measure Oscillation Amplitude! • 4 decay rates to fit the unknown parameters: • Ration between diagrams: r • Strong phase: d • Weak phase: g • Same experimental dilutions as in Dsp should be added: • Use the value of A, wtag and dt as obtained with Dsp fit… Bs→Ds-K+ Bs→ Ds-K+ Bs→Ds+K- Bs→ Ds+K-

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

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

  9. LHCb tracking: vertex region  • VELO: resolve Dms oscillations in e.g. Dsp events

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

  11. LHCb tracking: momentum measurement By[T] • Tracking: Mass resolution for background suppression in eg. DsK  Total Bdl = 4 Tm Bdl Velo-TT=0.15 Tm

  12. LHCb tracking: momentum measurement ~1.41.2 m2 All tracking stations have four layers: 0,-5,+5,0 degree stereo angles. ~65 m2 

  13. LHCb Hadron Identification: RICH 3 radiators to cover full momentum range: Aerogel C4F10 CF4 • RICH1 • 5 cm aerogel n=1.03 • 4 m3 C4F10 n=1.0014 • RICH2 • 100 m3 CF4 n=1.0005  • RICH: K/p separation e.g. to distinguish Dsp and DsK events.

  14. LHCb calorimeters e h  • Calorimeter system to identify electrons, hadrons and neutrals and used in the L0 trigger: hadron Pt trigger for Dsh events

  15. LHCb muon detection m  • Muon system to identify muons and used in L0 trigger e.g. unbiased trigger on “other B” for Dsp events

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

  18. Charged multiplicity distributions at generator level In LHCb acceptance ( 1.8 < h < 4.9 )

  19. The LHC environment • pp collisions @ s=14 TeV • Bunch crossing @ 40MHz • 25 ns separation • sinelastic = 80mb • At high L >>1 collision/crossing • Prefer single interaction events • Easier to analyze! • Trigger • Flavor tagging • Prefer L ~ 2 x 1032 cm-2s-1 • Simulate 10 hour lifetime,7 hour fill • Beams are defocused locally • Maintain optimal luminosity even when Atlas & CMS run at 1034

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

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

  22. Event example: detector hits

  23. Event example (Vertex region zoom)

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

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

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

  27. Robustness Test: Quiet and Busy Events • Monitor efficiency and ghost rate as function of nrel: “relative number of detector hits” • <nrel> = 1

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

  29. Experimental Resolution Momentum resolution Impact parameter resolution sIP= 14m + 35 m/pT dp/p = 0.35% – 0.55% 1/pT spectrum B tracks p spectrum B tracks

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

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

  32. Trigger Acceptance function • Impact parameter cuts lead to a decay time dependent efficiency function: “Acceptance” Acc Bs→DsK

  33. Bs→Dsh Reconstruction p 144 mm ,K 47 mm K Bs K Ds  d 440 mm • Final state reconstruction • Combine K+K-p- into a Ds- • Good vertex + mass • Combine Ds- and “bachelor” into Bs • Good vertex + mass • Pointing Bs to primary vtx Mass distribution: K/p separation

  34. Annual Yields and B/S • Efficiency Estimation: • Background Estimation: • Currently assume that the only background is due to bb events • Background estimates limited by available statistics • Estimation of Bs→Dsp background in the Bs→DsK sample: B/S = 0.111 ± 0.056

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

  36. Flavour tag sources for wrong tags: Bd-Bd mixing (opposite side) b → c →l (lepton tag) conversions… εtag [%] Wtag [%] εeff [%] Combining tags Bdp p 42 35 4 BsDs h 54 33 6 K+ • Knowledge of the B flavour at production is needed for the asymmetries Ds- B0 tagging strategy: • opposite side lepton tag ( b →l) • opposite side kaon tag ( b → c → s ) (RICH, hadron trigger) • same side kaon tag (for Bs) • opposite B vertex charge tagging B0 D K- l b Bs0 s b s K+ u u effective efficiency: eff= tag(1-2wtag)2

  37. Sensitivity Studies • Many GEANT events generated, but: • How well can we measure Dms with Bs→Dsp events? • How well can we measure angleg with Bs→DsK events? as function of Dms, DGs, r,g,d, and dilutions wtag, dt, …? • 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. Toy Generator • Generate events according to the “master” formula for B decay Bs→Ds-K+ Bs→Ds-K+ Relevant physics parameters: Bs→Ds+K Bs→Ds+K- With: For Ds+K-: replace gby-g For Dsp: Simplify: r=0

  39. Toy Simulation • Smear theoretical events (t=ttrue) into experimental events (trec) and assign an experimental error (dtrec). Method: • From the full simulation make a lookup table with selected events: ttruei, treci, dtreci • Generatettrue in toy and assign trecanddtrec from look-up table, such that non-Gausian effects of the full simulation are included • Foretagfraction of the events assign an event tag: • Statistically assign 1-wtagcorrect tags,andwtagwrong tags. • Current studiesetag= 54% wtag = 33% . • Apply an acceptance function A(trec)by statistically accepting events according to the acceptance value for a given event time.

  40. Dilutions in Bs→Dsp • Plot the MC toy decay rate with the following situation: 1 year data Bs→Ds-p+ • Experimental Situation: • Ideal resolution and tag

  41. Dilutions in Bs→Dsp • Plot the MC toy decay rate with the following situation: 1 year data Bs→Ds-p+ • Experimental Situation: • Ideal resolution and tag • Realistic tag

  42. Dilutions in Bs→Dsp • Plot the MC toy decay rate with the following situation: 1 year data Bs→Ds-p+ • Experimental Situation: • Ideal resolution and tag • Realistic tag • Realistig tag and resolution

  43. Dilutions in Bs→Dsp • Plot the MC toy decay rate with the following situation: 1 year data Bs→Ds-p+ • Experimental Situation: • Ideal resolution and tag • Realistic tag • Realistig tag and resolution • Realistic tag + reso + background

  44. Dilutions in Bs→Dsp • Plot the MC toy decay rate with the following situation: 1 year data Bs→Ds-p+ • Experimental Situation: • Ideal resolution and tag • Realistic tag • Realistig tag and resolution • Realistic tag + reso + background • Realistic tag+reso+bg+acceptance

  45. The signal for Dsp and DsK • The CP signal is not self-evident • Use full statistical power in the data 5 years data: Bs→Ds-p+ Bs→ Ds-K+ (Dms = 20)

  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 Pull distr Error distr

  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 trecwith measurement error dtrec= • Thus the likelihood of observing an event (trec, dtrec) = • Fit the physics parameters (Dm, g,…) in R such that the likelihood is maximal:.i.e. maximize:

  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 p and 4 decay rates for Bs-> Ds K Event probab: 1 year data: Bs -> Ds-p+ Bs -> Ds- K+ (Slow computation!) Normalization of the probability: Create the Likelihood: Fit parameters: -Physics: -Experimental: Normalization of the Likelihood is interesting! See also LHCb note…LHCb 2003-124 (Include information of the relative overall rates)

  49. Strategy for Dsp / DsK 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: • Calibrate the experimental time resolution • Fit the acceptance function on the untagged sample of Bs->Dsp events • Fit simultaneously the values ofDms, wtag with Dsp events. • Fit the values of the r, g, d with the DsK sample

  50. 1.Fitting the measurement errors L1 trigger • Resolution can be determined from the negative tail of the lifetime distribution. Fit with 10% of 1 year data: S·dtrec . => S = 0.99 ± 0.04 • Can L1 trigger be tuned to provide unbiased Bs-> Dsp events? • What would be the required bandwidth for this? • In any case unbiased samples of J/y events are foreseen. 10% of 1 year untagged Bs→Dsp S=0.99+- 0.04 trec

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