1. Mirko Berretti - Giuseppe Latino (University of Siena & INFN-Pisa) Track and Jet reconstruction in T2 “Forward Physics at LHC with TOTEM” Penn State University University Park, PA 04-28-2008 1

2. 2 T2 Track Reconstruction Preliminary Studies  T2 Track Reconstruction Preliminary Studies Highlights Topological Jet Algorithms: Cone-like & Kt-like  Topological Jet Algorithms: Cone-like & Kt-like T2 Tracking algorithm : Linear Least Square Method, T2 Tracking algorithm : Linear Least Square Method, based on T1 Tracking Algorithm (Fabrizio Ferro) based on T1 Tracking Algorithm (Fabrizio Ferro) Detailed studies on BP effects generating    and  0 at different  Detailed studies on BP effects generating    and  0 at different  Algorithms developed at charged particle level on Pythia di-jet Algorithms developed at charged particle level on Pythia di-jet events (5.3 3 GeV, events (5.3 3 GeV, no beam remnants contribution no beam remnants contribution Fake-Jet incidence studies on Single Diffractive events Fake-Jet incidence studies on Single Diffractive events Studies extended at track level Studies extended at track level

3. TOTEM Detectors: Setup in CMS CMS HF RP1 (RP2) RP3 220 m (180 m) 147 m Elastic Detectors (Roman Pots): Elastic Detectors (Roman Pots): position of p scattered elastically at small angles Active area up 1-1.5 mm from beam: 5-10  rad T1:  3.1 <  < 4.7 T2: 5.3 <  < 6.5 Inelastic Telescopes: reconstruction of tracks and interaction vertex  T1: 18 - 90 mrad T2: 3 - 10 mrad  = - log(tg(  /2))  ~14 m 10.5 mT1T2 Detectors on both sides of IP5 3

4. 4 Beam Pipe Effect: Secondary Particles 1 Each point = Each point = per event firing  - with: E = 50 GeV     as shown in x axis Effect of beam pipe (I) : pion hadronic interaction  lots of secondary particles T2 cone section at η = 5.53 cone section at η = 4.9

5. A cut on track Z@ R min allows to remove a big fraction of secondaries: given the track eta, the curves below can be used to decide wether the track is from the primary vertex or not The distribution of tracks Z@R min depends on track  and particles energy Importance of Z@R min Distribution 4 5  RECO 22

6. Particle Energy Distributions in T1-T2  Region (Important for Future Analyses Strategies) 6 For example: potential losses of low energy tracks reconstruction in di-jet events are less important than in SD events or minimum bias events

7.  - E = 50 GeV for each point Importance of Z@R min Distribution Using the curve at E = 50 GeV in previous slide almost all secondaries are removed requiring DZ=Z@R min < ,   ,  REC in T2 DZ=Z@R min DZ=Z@R min 7 Efficiency = mean number of tracks per event with    1, Z@R min <  5.2  rec < 6.6

8.  &  Resolution with B.P. & M.F.: 1000  , E = 50 GeV, 0 <  < 2  8  GEN outside critical zones  GEN outside critical zones  GEN inside critical zones  GEN inside critical zones Reconstructed depends on track energy because of M.F. effect

9.  &  Resolution with B.P. & M.F.: 1000   E = 50 GeV, 0 <  < 2  9 Each point obtained from a gaussian fit on a histogram like the ones shown in previous slide    (deg)    “Intrinsic”  resolution: rec depends on track rec depends on track energy because of M.F. effect energy because of M.F. effect

10. 10   Contribution     track  Effect of beam pipe (II) :  (from  0 ) e.m. interaction  lots of secondary particles 10K  0 generated with: E = 50 GeV 5.2 <  < 6.5, 0 <  < 2  Potential effect of secondaries from CMS HF calorimeter to be investigated

11. 11 We want to study the possibility to develop a jet algorithm by using only charged particles reconstructed with T1/T2 To do this, we have to exchange the (usual) P T information with the particle density information in the  plane First step: the algorithm has been developed/tested by using Pythia di-jets events where both partons are required to fall in the T2 region (5.3 <  < 6.5). Topological Jet Algorithms Topological Jet Algorithms

12. We have developed a “cone” algorithm with some improvement, based on MidPoint algorithm Cone jet algorithm: implementation 12 Build  grid Fill the cells with the number of particles inside them Construction of ordered list (in particle number) of seed avoiding to include the neighbouring cells of the major seeds From the seeds start the search of stable cones (1st Jet list)‏ Merging - Splitting 2nd Jet List multiplicity and shape cuts Final Jet List Mid Point Jets ordered according to N trk

13. Summary of current Cone Jet Algorithm Parameters (to be optimized according to specific analysis needs) Cone search-radius (R) 0.7 Grid Size 0.4 Percentage of particles shared between two jets for merging-splitting: 50 % of the less populated jet If the number of shared particles is less than half multiplicity of the less populated jets, the two jets are split otherwise are merged. Jet Charged Multiplicity cut : 2 Jet Shape request: none 13

14. Features The Kt algorithm is in the Cambridge/Aachen implementation and it is based on “FastJet Algorithm” arXiv:hep-ph/0512210. It does not use particle transverse momentum information, only  distance Rij: A “Kt-like” Jet Algorithm also Developed: The merging criteria for each couple of particles of our algorithm (particles topologically clusterized to make a jet) gives similar results of a “traditional” kt algorithm (that also uses transverse momentum information). 14

15. Kt Jet Algorithm: Implementation no yes update the jet list Build a list of all the particles (proto-jets) For each particle i, find the particle j with minimum Rij. i is a Jet Merge i,j in a single jet multiplicity and shape cuts Rij > R Final Jet List yes Remove i from the jet list empty list no 15 Present algorithm setting: R = 1 No shape cut Trk multiplicity = 2 Jets ordered according to N trk

16. First test with 1000 di-jet events (5.0 3 GeV) at track level The configuration files shown below set the algorithm working parameters. NOTE: they are not yet optimized in order to maximize reconstruction efficiency and background rejection 16 Kt configuration file module Ktjetfinder = Ktjf { double T2minEta= 5.1 double T2maxEta= 6.6 double T1minEta= 3.2 double T1maxEta= 4.7 double Range1m= 5.35 double Range1M= 6.15 double NumSigZ= 3.0 double Chicut=1.0 double GlobalRkt= 1.0 double Beamtreshdist= 1.0 double Jetradiusforshape= 0.5 double ThresholdShape= 0.0 double Shapepar=0.5 uint32 ThresholdMult= 2 } Cone configuration file module Conejetfinder = Conejf { double T2minEta= 5.0 double T2maxEta= 6.6 double T1minEta= 3.2 double T1maxEta= 4.7 double Chicut=1.0 double Range1m=5.35 double Range1M=6.15 double NumSigZ=3.0 double minetagrid=2.5 double maxetagrid=7.0 double minphigrid=0.0 double maxphigrid= 6.283185 double cellphi=0.4 double celleta =0.4 double SearchRadius =0.7 double ThresholdShape= 0.0 double Shapepar=0.5 uint32 ThresholdMult= 2 } Parameters for “track-quality”selection Parameters for Parameters for“jet-quality”selection “Internal” working algorithm parameters algorithm parameters Kt & Cone Jet Algorithms (Track and Particle Level Comparison)

17. Distance Jet-Nearest Parton (Kt) Particle Level Track Level 17

18. 18 Di-jet Reconstruction Efficiency Particle Level Track Level Cone Kt NOTE: efficiency ~ 50% (at PL) if no cut on T1/T2  acceptance

19. 19 Fake Jet Reconstruction Efficiency in SD Events Particle Level Track Level Cone Kt

20. Summary & Conclusions Track reconstruction with typical efficiency well above 80% for charged  First studies on track selection criteria performed Secondary tracks from B.P. interaction efficiently removed with standard cut criteria Cone-like and Kt-like jet algorithms developed and tested Both algorithms show similar behaviour with jet reconstruction efficiency for di-jet events around 25-30% and fake rate in SD events around 5% (preliminary results) Possibility of improvements in jet selection (jet parameter optimization) and in background rejection (for instance considering angular correlation or combining energy information from Castor….) 20 Jet Reconstruction T2 Track Reconstruction (Preliminary Results)

21. BACKUP SLIDES

22. T2Road : “histogramming method” Build an integer matrix in R-phi plane Build an integer matrix in R-phi plane (at the moment cell size: [2.0 mm]x[3 deg]) Increment the matrix cell value according to Increment the matrix cell value according to the number of hits falling inside the number of hits falling inside Build the roads clusterizing the hits around the Build the roads clusterizing the hits around the “seed cells” (=cells with #hits > threshold) at the moment road size: [6.0 mm]x[9 deg] at the moment road size: [6.0 mm]x[9 deg] B1 T2Track : LeastLSQ on T2Road (copied & adapted from T1 Tracker) 1Road 1 Track  R min Z at Rmin Reduced   Main Track Parameters Straight track in 3D are found looking for its projection in YZ and XZ plane Minimum distance Reconstructed Track-Z Axis (Z Axis =beam pipe Axis)

23. B2 The Beam pipe Effect : Reconstructed Track  Track  for 1000    with E=50 GeV fired at  (left) and  right   (left) and  right  (expected interaction with the  b.p. cone section)

24. B3 The Beam pipe Effect : Reconstructed Track   Suggested cut: Tracks belonging to primary particles have    1 Track    for   with E=50 GeV fired at  (left) and  right  (expected interaction with the  b.p. cone section)‏

25. Detailed Z@ Rmin distribution studies inside “critical zones” Detailed Z@ Rmin distribution studies inside “critical zones” B4 Close to detector edge (low  edge (low  ) Close to detector edge (high  )  close to b.p. cone section  outside critical zones

26. To use a reasonable grid size and search radius we have looked at the distribution of charged particles around the outgoing parton B5

27. Angular resolution (all cuts activated)‏ Find the best cell size Check the algorithm stability shifting the grid Other features (CONE) B6

28. Mid - Point For every pair of jets with distance < 2R, assign a new seed in the midpoint. This method can reduce “infrared-like” sensitivity of our algorithm due to missing   s in the core of particle jet and “collinear-like” sensitivity due to the choice on grid size/position. Algorithm start from central most populated cell and it will probably find 3 jets or only 1 jet (if the jet are too close in the preclustering). Without Mid-Point, algorithm starts from the right-most cell and it will find only 2 jets. With Mid Point it will find 3 jets or 1 jet Collinear-like sensitivity Infrared-like sensitivity With Mid-Point we can merge two different jets when soft radiation/lost particle is present between them B7

29. We need some jet “quality” cuts Multiplicity (minimum number of particles forming a jet) >= 2 Shape Jet charged shape: # charged particles inside cone 0.4 # charged particles inside cone 0.7 > 0,5 Mult. cut Shape cut ? B8

30.  distribution (all Jets) Particle Level Track Level Cone Kt B9

31. B10  distribution (all Jets) Cone Kt Particle Level Track Level

32.  distribution for event with 2 Jet reconstructed Cone Kt Particle Level Track Level B11

33. Minimum J1-J2   separation for events with 2 Jet reconstructed Cone Kt Particle Level Track Level B12

34. Distance Jet-Nearest Parton (Cone) Particle Level Track Level B13

35. B14 Minimum  Separation, Fake Jet in SD Events

36. B15  Separation, Fake Jet in SD Events

37. B16 Track Multiplicity in Jet

38. Energy of Charged Particles Inside Jet in T2 B17