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

2. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Outline Introduction Impact parameter based tags Secondary vertex based tags Multi-jet studies Trigger studies

3. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa 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

4. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Introduction Example:Example: Effects on h  bb decay reconstruction in MSUGRA

5. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa The problem definition How a “real” 2-jet event looks like:

6. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Tags Ingredients 1.Track reconstruction 2.Transverse and longitudinal impact parameter 3.Primary vertex reconstruction in z 4.Jet reconstruction 5.Vertex reconstruction

7. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Impact parameter Linearise track @ point of closest approachLinearise track @ point of closest approach Sign positive if the track-jet crossing point is downstreamSign positive if the track-jet crossing point is downstream NeedNeed Jet Primary vertex Tracks Track decay length

8. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Track Reconstruction <10 -5 Efficiency for particles in a 0.4 cone around jet axis E T = 200 GeV Fake Rate < 8 *10 -3 E T = 50 GeV Fake Rate < 10 -2

9. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Track Selection cuts –3 pixel + 5 silicon (at least) –p T >1 GeV/c –D 0 <2 mm –Cone size 0.4

10. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Primary vertex reconstruction Track seeding findingTrack seeding finding –Hits in the innermost layers are matched in r-  and r-z –Pixel seeds formed if transverse i.p. < 1mm and within the luminous region in z PV findingPV finding –Clusters of tracks along the beam axis –PV candidate: largest number of tracks with highest scalar p T sum Using full Tracker reconstruction Using full Tracker reconstruction –Combinatorial algorithm  2 based rejection

11. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Primary vertex reconstruction  = 26  m Pixel - Resolution in z (cm)  Using only the Pixels: fast, resolution ~ 30  m in z (QCD events)  Using full Tracker: slower, better resolution ~15  m in z (uu events) Full Tracker- Resolution in z (cm)

12. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Jet reconstruction Calorimetry data organized in towers (HCAL  0.087x 0.087 barrel,  0.175 x 0.175 end-caps, 25 crystal ECAL -> 1 HCAL tower). 25 crystal ECAL -> 1 HCAL tower). Iterative cone algorithm with calo (ECAL+HCAL) tower as input. Proto-jet is defined as Et =  Et i,  =   i Et i /  Et i  =   i Et i /  Et i Iteration until |Et n+1 –Et n |<      

13. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Jet Cone and Tracks Selection bb uu Optimize cone sizeOptimize cone size

14. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Impact Parameter Significance 3 dim Simply tag jets by requiring a minimum number of tracks exceeding a given i.p. significance 2 dim

15. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Simple impact parameter Tag

16. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Impact parameter Probability Tag Originally developed by ALEPHOriginally developed by ALEPH Tracks with negative impact parameter d can be used to measure the intrinsic resolution Confidence level that a track with impact parameter significance S originates from the primary vertex : Impact parameter significance

17. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Impact parameter Probability Tag The probability that a set of tracks is coming from the primary vertex can be computed as By constructionBy 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

18. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Impact parameter Probability Tag Divide tracks into classes depending on p and 

19. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Confidence levels 2 dim 3 dim 100 GeV Barrel

20. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Probability tag Performance

21. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Secondary vertex based tags Fast Reconstruction ÊLinearise tracks around the origin (valid if secondary vertex not too far and if p T is sufficiently large)  For each track measure the transverse impact parameter d 0 and its azimutal angle  which are related with the vertex position (l,  B ) Each track coming from the same secondary vertex has the same l and  B d 0 = l sin(  -  B )  l (  -  B )  d0d0 BB l Track Sec. Vtx Origin  Primary vertex x y

22. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa The d 0 -  plane Tracks coming from the same secondary vertex –have relatively large d 0 –are aligned on a positive slope segment Tracks from origin lie around d 0 ~0 and at any  angle In the d 0 -  plane a track is a point d0d0  B tracks P.V. tracks Positive slope d 0 = l  - l  B A typical event

23. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa How to find seeds ÊLinks Segment connecting 2 tracks close in  and  positive slope ËClusters a 2-track cluster is a link check if 2 links are close in the d 0 -  -  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 Good Links  d0d0 Bad Link Cluster d 0 = l  - l  B

24. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Backgrounds Interactions in the beam pipe Radial distance (cm)

25. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Backgrounds Number of tracks in the vertex (Barrel region, E T =100 GeV) Tighten cuts on 2 tracks’ vertices: Require positive impact parameter to tracks belonging to vertices

26. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Secondary Vertex tags Performance Decay length significance (before all other cuts applied) decay length significance in 3-dimSimple selection based on decay length significance in 3-dim

27. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Secondary Vertex Tags Performance

28. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Tracks’ Tunings Track counting algorithm Optimize this

29. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Tracks’ Tunings Probability Tag algorithm Optimize maximum track probability

30. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Tracks’ Tunings Secondary Vertex Tag algorithm Optimize track impact parameter sign

31. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Comparisons between algorithms

32. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Comparisons between algorithms

33. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Charm jets

34. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Comparisons between algorithms - charm

35. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Comparisons between algorithms - charm

36. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Tag correlations Impact parameter significance Secondary vertex significance

37. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa 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

38. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Conditional Track Reconstruction

39. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Recipe for B inclusive triggers 1.From pixel hits and calorimeters: –The seed for tracks reconstruction is created around the LVL1 jet direction –Primary vertex is calculated 2.Tracks are reconstructed in a cone of  R<0.4 around the jet direction 3.Tracks are conditionally reconstructed 4.Refine the jet direction by using the reconstructed tracks

40. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa L1+Tracks B-tag E t =100 GeV jets barrel 0.<| η |<0.7 Online performance is better with L1+Tk jets!! OFFLINE HLT Jet-tag: 2 tracks with S IP >0.5,1.,1.5,2.,2.5,3.,3.5,4.

41. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Jet reconstruction L1 jets η L2 jets η L1 jets + Tk η L1 jets φ L2 jets φ L1 jets + Tk φ σ η =0.112 σ η ~0.037 σ η ~0.025 Raw Calo Level 1Raw Calo Level 1 Calorimeter Level 2 jetsCalorimeter Level 2 jets Calorimeter Level 2 + TracksCalorimeter Level 2 + Tracks

42. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Sign flip of IP L1 jet (poor) resolution in η and φ ( σ ~0.1) 2d transverse IP sign flip η rec - η sim σ η ~0.1 u b OFFLINE – Lucell HLT-L1 Jets

43. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Impact Parameter Sign flip for bad jet axis resolution –Tracks assigned to a completely off jet For close by jets Very large rapidity b jets –Enhanced by badly reconstructed Primary Vertex For short lived or low momentum B’s

44. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa L1+Tracks B-tag (2) E t =100 GeV jets barrel 0.<| η |<0.7 Better b jets efficiency with 3d IP Jet-tag: 2 tracks with S IP >0.5,1.,1.5,2.,2.5,3.,3.5,4. OFFLINE HLT

45. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Timing for b jets Expect to gain at least factor 2

46. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Timing for u jets Expect to gain at least factor 2

47. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Efficiency for b jets

48. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Efficiency for u jets

49. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa RoI L2 jets uubbcc E t = 100 GeV barrel

50. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa B-tag performance offline HLT offline HLT Impact Parameter Significance Tag (not optimised)

51. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Inclusive Jet Rate Inclusive HLT jet rate p t = 50÷170 GeV 2.4 KHz @ 120 GeV ^

52. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Number of B’s and C’s in the central region Fraction of events with at least 1 b or c jet: ƒ b>0 ~6% ƒ c>0 ~11% with at least 2 b or c jets: ƒ b>1 ~1.6% ƒ c>1 ~2.4% All c b

53. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Inclusive jet Rate and tag  2 jets inside Tracker E jet >25 GeV Tag: 2x3 

54. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa A tour in B physics … B  J/  reconstruction p t <2 GeV @ 5σ hit=5 or σ(p t )/p t <0.02 max n. of cand=2 Overall efficiency ~11% Background rate: from 16 to 0.4 Hz

55. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa J/  mass resolution Partial reco Full reco

56. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Vertex reco Full Partial x vertex resolution (  m)

57. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Conclusions Several robust tags available with good performances –More tags still in implementation (leptons …) HLT looks promising –Detailed investigations for performance at high luminosity

58. B  Workshop Helsinki 2002 b Tagging with CMSFabrizio Palla INFN Pisa Conclusions Helsinki temperature In a few time btag activities will rise in as Helsinki temperature!