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LHCb Simulation Studies: From Detector Optimization to Data Preparation

LHCb Simulation Studies: From Detector Optimization to Data Preparation. SAC Review Meeting May 20, 2005 Marcel Merk. The “Tracking and Physics” Team. The NIKHEF LHCb software “team” 2005: Staff : M. M., G. Raven Postdoc (CERN based) : E. Rodrigues

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LHCb Simulation Studies: From Detector Optimization to Data Preparation

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  1. LHCb Simulation Studies:From Detector Optimization to Data Preparation SAC Review Meeting May 20, 2005 Marcel Merk

  2. The “Tracking and Physics” Team • The NIKHEF LHCb software “team” 2005: • Staff: M. M., G. Raven • Postdoc (CERN based): E. Rodrigues • Graduate students: E. Bos, B. Hommels, S. Klous, J. Nardulli, G.Ybeles Smit, J.v.Tilburg, M. Zupan. • Undergraduate students: J. Amoraal, B. M’charek • NIKHEF software activities embedded in: • The LHCb Computing Project (M.M. convenor “Track Fitting”) • The Physics Planning Group (G.Raven convenor “Proper time and mixing”) • Graduate Student “Model” • ~ 2 years contribution to hardware or software • ~ 1 year contribution to physics studies • ~ 1 year thesis writing + other

  3. Past Studies • Theses • (2001) Thesis N. Zaitsev (Pile-up and Bs→J/yf) • (2002) Thesis R. v.d. Eijk (OT and tracking) • (2003) Thesis R. Hierck (Tracking and Bs→DsK/p) • (2004) Thesis N. v. Bakel (Velo and Bs mixing) • Past Simulation studies to optimize LHCb • Event yields vs. hadronic interaction lengths • Pattern recognition vs. detector occupancy • Resolutions vs. multiple scattering • LHCb Classic => LHCb Light …

  4. Evolution since Technical Proposal • Reduced • material • Improved • level-1 trigger

  5. Present Studies • Present Studies: preparations for data • OT DAQ simulation and decoding • Track pattern recognition • Track fitting and alignment • Lifetime reconstruction • Bs oscillation and CP violation extraction • Our physics motivations include: • Bs oscillation with Bs→ Dsp Dms • CP violation with with Bs→ DsKCP angle g – 2c • Search for new physics with Bs → J/yf Bs mixing angle 2c • Study of rare decays with b→s l+l-b→s penguin • Illustrate our studies using the example of the decays • Bs→ Dsp and Bs→ DsK

  6. 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. • “high” Pt tracks and displaced vertices • displaced vertices • Efficient trigger • Select the B decay, reject background: • Mass resolution • Tag the flavour of the B decay • Tagging power • Plot the tagged decay rate as function of the decay time • Decay time resolution

  7. 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

  8. 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 +

  9. 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 + 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-

  10. The expected signal for Dsp and DsK • Nominal expectations for • Efficiency • Background • Resolution • Tagging power • Etc. • Bs mixing relatively easy • CP signal is not self-evident • Use full statistical power in the data 5 years data: Bs→Ds-p+ Bs→ Ds-K+ (Dms = 20) (g = 65 degrees) Measure frequency Measure amplitude

  11. 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 • J.Nardulli, J.v.Tilburg: Geometry and MC Hits for the Outer Tracker • Detector Response (“digitization”): • Boole: Converting the MC Hits into a raw buffer emulating the real data format • B.Hommels, J.Nardulli, A.Pellegrino: L1 and DAQ data format Outer Tracker • Reconstruction: • Brunel: Reconstructing the tracks from the raw buffers. • E.Bos, H.Hommels, M.M., J.Nardulli, G.Ybeles Smit, J.v.Tilburg • Physics: • DaVinci: Reconstruction of B decays and flavour tags. • LoKi : “Loops and Kinematics” toolkit. • J.Amoraal, S.Klous, B.M’charek, G.Raven, J.v.Tilburg, M.Zupan, • Visualization: • Panoramix: Visualization of detector geometry and data objects • J.v.Tilburg: Display of tracks

  12. The LHC environment • pp collisions @ s=14 TeV • s (inel)=79.2 mb, s (bb)=633 mb • 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

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

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

  15. Event example: detector hits J.v.Tilburg

  16. Event example (Vertex region zoom)

  17. 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- J.Nardulli, J.v.Tilburg TDC spec.: 1 bunch + Spill-over + Electronics + T0 calibration

  18. Track finding strategy T track Upstream track B. Hommels G. Ybeles Smit N. Tuning T seeds VELO seeds Long track (forward) Long track (matched) J.v.Tilburg VELO track Downstream track R.Hierck 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)

  19. 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

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

  21. Kalman Track Fit Momentum pull distribution: s = 1.0 s = 1.2 z E.Bos. M.M., E.Rodrigues, J.v.Tilburg • Reconstruct tracks including multiple scattering. • Main advantage: correct covariance matrix for track parameters!! Impact parameter pull distribution:

  22. 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

  23. 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 OT in L1: B.Hommels, N.Tuning

  24. BMass Reconstruction J.v.Tilburg, B.M’charek S.Klous, J.Amoraal M.Zupan 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

  25. Annual Yields and B/S for Bs→Dsh • 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

  26. 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!

  27. Sensitivity Studies M.M., G.Raven, J.v.Tilburg • 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

  28. 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

  29. 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

  30. 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

  31. 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

  32. 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

  33. 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

  34. Fitting time dependent decay rates • Use unbinned Likelihood fitter • Why use complicated method? • Weigh precisely measured events differently from badly measured events • Rely on the reconstructed event error • Allow for a scale factor and bias in the analysis Error distr Pull distr

  35. Fit the Physics parameters in Dsp and DsK M.M. • Use the 4 tagged (B) and (B)Dsp decay rates to fit Dms and Wtag fraction • Use the 4 tagged DsK events to fit r, g, d 5 years data: Bs→Ds-p+ Bs→ Ds-K+ (Dms = 20) • Actually perform the Dsp and DsK fits simultaneous • For each setting of the parameters repeat ~100 toy experiments • A task for the GRID

  36. The sensitivity of Dms after 1 year • Precision on Dms in ps-1 • The sensitivity for Dms • Amplitude fit method analogous to LEP • Curves contain 5 different assumptions for the decay time resol. 5s • Sensitivity: Dms = 68 ps-1 ~1000 jobs

  37. CPanglegsensitivity for many parameter settings (Ab-)using the GRID • Precision on angle g after one year with 1 year data: s(g)~ 14o Dependence on background Dependence on resolution

  38. Bs mixing phase and b→s penguin reconstructed matched to generated decay J.Amoraal, S.Klous • Bs →J/yf • Admixture of CP even and CP odd final states • Sensitive to Bs mixing phase • b→s m+m- • b→s decay (Afb) is sensitive to SUSY parameters • Inclusive event selection M. Zupan

  39. Summary • NIKHEF LHCb group has a relatively large involvement in software • Past: Detector Optimization (4 Theses: N.Z.:2001, R.v.d.E.:2002, R.H.:2003, N.v.B.:2004) • Now: Preparation for Data • Reconstruction Responsibilities (convenor: “Track fitting”)(M.M.) • OT simulation and detector response (J.v.Tilburg, J.Nardulli) • OT region pattern recognition (Online and Offline) (B.Hommels, G.Ybeles Smit) • Kalman track fitting (E.Rodrigues, J.v.Tilburg) • Alignment studies (E.Bos, J.Nardulli) • Physics Responsibilities (convenor: “Proper time and mixing”)(G.Raven) • Measurement of Dms with Bs→ Ds p(J.v.Tilburg) • Measurement of g-2c with Bs→ DsK (J.v.Tilburg, B.M’charek) • Measurement of 2c with Bs→ J/yf (S.Klous, J.Amoraal) • Study of rare decays with b → s l+l-(M. Zupan)

  40. Outlook • A possible scenario before the LHCb measurement of g:

  41. Outlook • A possible scenario after the LHCb measurement of g:

  42. The End (Some X-tra slides)

  43. B Physics: A (quick) comparison • Comparison with e+e- factories: • All b hadrons produced: Bu (40%), Bd(40%), Bs(10%), Bc and b-baryons (10%) => Bs physics! • Statistics vs Systematics • B hadrons not coherent: mixing dilutes tagging • Many particles not associated to b hadrons: primary vertex • Decay time resolution • Rare decays • Comparison with hadronic facilities: • CDF & D0: • S/B • Dedicated trigger • PID • BTeV: ~ equivalent • S/B • ECAL • Vtx+Trigger

  44. Efficiencies, event yields and Bbb/S ratios Nominal year = 1012 bb pairs produced (107 s at L=21032 cm2s1 with bb=500 b) Yields include factor 2 from CP-conjugated decays Branching ratios from PDG or SM predictions

  45. CP Asymmetries and Dilutions Both mis-tags (w) & finite proper time resolution (σt ) dilute CP asymmetries. A simplified model (tested on toy MC): A plausible scenario: In this (Bs) case σt dominates dilution error, and total systematic significant! (eg. our expected annual statistical precision on Af for DsK is 0.05 [CHECK]) Hence, we are now investigating ways to maximise understanding of tagging, proper time resolution and acceptance, and trigger biases. (Needless to say, any improvement in performance is also useful !)

  46. LHCb

  47. 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

  48. LHCb detector ~ 200 mrad ~ 300 mrad (horizontal) 10 mrad p p  • Inner acceptance ~ 15 mrad (10 mrad conical beryllium beampipe)

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

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