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b Tagging with CMS

b Tagging with CMS. Fabrizio Palla INFN Pisa B  Workshop Helsinki 29 May – 1 June 2002. Outline. Introduction Impact parameter based tags Secondary vertex based tags Multi-jet studies Trigger studies. Introduction.

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b Tagging with CMS

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  1. b Tagging with CMS Fabrizio Palla INFN Pisa B Workshop Helsinki 29 May – 1 June 2002

  2. Outline • Introduction • Impact parameter based tags • Secondary vertex based tags • Multi-jet studies • Trigger studies b Tagging with CMS

  3. Introduction • Lot of B hadrons in the final state from interesting physic processes • Top • Higgs • Supersymmetry • B tag relies upon the long lifetime and large mass b Tagging with CMS

  4. Introduction • Example: Effects on h bb decay reconstruction in MSUGRA b Tagging with CMS

  5. The problem definition How a “real” 2-jet event looks like: b Tagging with CMS

  6. Tags Ingredients • Track reconstruction • Transverse and longitudinal impact parameter • Primary vertex reconstruction in z • Jet reconstruction • Vertex reconstruction b Tagging with CMS

  7. Impact parameter • Need • Jet • Primary vertex • Tracks • Linearise track @ point of closest approach • Sign positive if the track-jet crossing point is downstream Track decay length b Tagging with CMS

  8. Track Reconstruction <10-5 Efficiency for particles in a 0.4 cone around jet axis ET = 200 GeV Fake Rate < 8 *10-3 ET = 50 GeV Fake Rate < 10-2 b Tagging with CMS

  9. Track seeding finding Hits in the innermost layers are matched in r-f and r-z Pixel seeds formed if transverse i.p. < 1mm and within the luminous region in z PV finding Clusters of tracks along the beam axis PV candidate: largest number of tracks with highest scalar pT sum Using full Tracker reconstruction Combinatorial algorithm c2 based rejection Primary vertex reconstruction b Tagging with CMS

  10. Primary vertex reconstruction • Using only the Pixels: fast, resolution ~ 30mm in z (QCD events) • Using full Tracker: slower, better resolution ~15mm in z (uu events) s = 26 mm Full Tracker- Resolution in z (cm) Pixel - Resolution in z (cm) b Tagging with CMS

  11. Jet reconstruction Calorimetry data organized in towers (HCAL h-f 0.087x 0.087 barrel, h-f 0.175 x 0.175 end-caps, 25 crystal ECAL -> 1 HCAL tower). Iterative cone algorithm with calo (ECAL+HCAL) tower as input. Proto-jet is defined as Et = S Eti , h = Shi Eti/S Eti • = Sfi Eti/ S Eti Iteration until |Et n+1 –Et n|<1% (Dh2 + Df2) <0.01 b Tagging with CMS

  12. bb uu Jet Cone and Tracks Selection Optimize cone size b Tagging with CMS

  13. Impact Parameter Significance 2 dim 3 dim Simply tag jets by requiring a minimum number of tracks exceeding a given i.p. significance b Tagging with CMS

  14. Simple impact parameter Tag b Tagging with CMS

  15. Impact parameter Probability Tag • Originally developed by ALEPH • Tracks with negative impact parameter d can be used to measure the intrinsic resolution Confidence levelthat a track with impact parameter significance Soriginates from the primary vertex: Impact parameter significance b Tagging with CMS

  16. Impact parameter Probability Tag By construction the track impact parameter C.L. for tracks coming from primary vertex is flat If a track comes from a displaced vertex its C.L. is very small Track confidence level The probability that a set of tracks is coming from the primary vertex can be computed as b Tagging with CMS

  17. Impact parameter Probability Tag Divide tracks into classes depending on p and h b Tagging with CMS

  18. Confidence levels 3 dim 2 dim 100 GeV Barrel b Tagging with CMS

  19. Probability tag Performance b Tagging with CMS

  20. y Track f Sec. Vtx l d0 fB x Origin  Primary vertex Secondary vertex based tags Fast Reconstruction • Linearise tracks around the origin (valid if secondary vertex not too far and if pT is sufficiently large) • For each track measure the transverse impact parameterd0and its azimutal anglefwhich are related with the vertex position (l,fB) Each track coming from the same secondary vertex has the same l and fB d0 = l sin(f-fB) l(f-fB) b Tagging with CMS

  21. B tracks P.V. tracks The d0-f plane • Tracks coming from the same secondary vertex • have relatively large d0 • are aligned on a positive slope segment • Tracks from origin lie around d0~0 and at any f angle In the d0-fplane a track is a point d0= lf -lfB Positive slope d0 A typical event f b Tagging with CMS

  22. d0= lf -lfB Cluster d0 Bad Link f Good Links How to find seeds • Links • Segment connecting 2 tracksclose in h and f • positive slope • Clusters • a 2-track cluster is a link • check if 2 links are close in the d0-f-h space  3-tracks cluster • Merge clusters with links in common  many tracks clusters • The vertex seeds are the clusters which remain at the end of the iteration b Tagging with CMS

  23. Interactions in the beam pipe Backgrounds Radial distance (cm) b Tagging with CMS

  24. Backgrounds • Tighten cuts on 2 tracks’ vertices: Require positive impact parameter to tracks belonging to vertices Number of tracks in the vertex (Barrel region, ET=100 GeV) b Tagging with CMS

  25. Secondary Vertex tags Performance Simple selection based on decay length significance in 3-dim Decay length significance (before all other cuts applied) b Tagging with CMS

  26. Secondary Vertex Tags Performance b Tagging with CMS

  27. Tracks’ Tunings Track counting algorithm Optimize this b Tagging with CMS

  28. Tracks’ Tunings Probability Tag algorithm Optimize maximum track probability b Tagging with CMS

  29. Tracks’ Tunings Secondary Vertex Tag algorithm Optimize track impact parameter sign b Tagging with CMS

  30. Comparisons between algorithms b Tagging with CMS

  31. Comparisons between algorithms b Tagging with CMS

  32. Charm jets b Tagging with CMS

  33. Comparisons between algorithms - charm b Tagging with CMS

  34. Comparisons between algorithms - charm b Tagging with CMS

  35. Tag correlations Secondary vertex significance Impact parameter significance b Tagging with CMS

  36. High Level Triggers • No b primitives at L1 • Start from L1 or L2 jets in the calorimeters • Aim to reduce the rate using b-tag at HLT b Tagging with CMS

  37. Conditional Track Reconstruction b Tagging with CMS

  38. Recipe for B inclusive triggers • From pixelhits and calorimeters: • The seed for tracks reconstruction is created around the LVL1 jet direction • Primary vertex is calculated • Tracks are reconstructed in a cone of DR<0.4 around the jet direction • Tracks are conditionally reconstructed • Refine the jet direction by using the reconstructed tracks b Tagging with CMS

  39. OFFLINE HLT L1+Tracks B-tag Et=100 GeV jets barrel 0.<|η|<0.7 Online performance is better with L1+Tk jets!! Jet-tag: 2 tracks with SIP>0.5,1.,1.5,2.,2.5,3.,3.5,4. b Tagging with CMS

  40. Raw Calo Level 1 ση=0.112 L1 jets η L1 jets φ Calorimeter Level 2 jets ση~0.037 L2 jets η L2 jets φ Calorimeter Level 2 + Tracks ση~0.025 L1 jets + Tk η L1 jets + Tk φ Jet reconstruction b Tagging with CMS

  41. L1 jet (poor) resolution in η and φ (σ~0.1) 2d transverse IP sign flip u OFFLINE – Lucell b ση~0.1 HLT-L1 Jets ηrec- ηsim Sign flip of IP b Tagging with CMS

  42. OFFLINE HLT L1+Tracks B-tag (2) Et=100 GeV jets barrel 0.<|η|<0.7 Better b jets efficiencywith 3d IP Jet-tag: 2 tracks with SIP>0.5,1.,1.5,2.,2.5,3.,3.5,4. b Tagging with CMS

  43. Timing for b jets Expect to gain at least factor 2 b Tagging with CMS

  44. Timing for u jets Expect to gain at least factor 2 b Tagging with CMS

  45. Efficiency for b jets b Tagging with CMS

  46. Efficiency for u jets b Tagging with CMS

  47. offline offline HLT HLT B-tag performance Impact Parameter Significance Tag (not optimised) b Tagging with CMS

  48. Inclusive HLT jet rate pt= 50÷170 GeV 2.4 KHz @ 120 GeV ^ Inclusive Jet Rate b Tagging with CMS

  49. Fraction of events with at least 1 b or c jet: ƒb>0~6% ƒc>0~11% c All b with at least 2 b or c jets: ƒb>1~1.6% ƒc>1~2.4% Number of B’s and C’s in the central region b Tagging with CMS

  50. 2 jets inside Tracker Ejet>25 GeV Inclusive jet Rate and tag Tag: 2x3s b Tagging with CMS

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