1. Tracking, PID and primary vertex reconstruction in the ITS Elisabetta Crescio-INFN Torino

2. The Inner Tracking System 6 layers Vertex reconstruction: SPD or tracks Tracking: 6 (5) layers PID: 4 layers (SDD+SSD) Pixel (SPD) Drift (SDD) Strip (SSD)

3. Weakly decaying beauty and charm states Need for high precision vertex detector  tracks from heavy flavour weak decays are typically displaced from primary vertex by ~ 100’s µm primary vertex decay vertex decay length = L  track impact parameter Vertex reconstruction in the ITS (1)

4. Pb-Pb collisions: beams well focused in the transverse plane and transverse position known from the machine monitoring system with a resolution of ~10  m reconstruction of z vertex (z = beam direction) pp collisions: reduction of the nominal luminosity to limit the pile-up by increasing β* or displacing the beams interaction diamond larger than 150  m, 3D vertex reconstruction Vertex reconstruction using SPD (vtxSPD)  for estimation of vertex before tracking->efficiency Vertex reconstruction using tracks (vtxTracks)  Precise reconstruction after tracking->precision Vertex reconstruction in the ITS (2)

5. Using SPD  correlation between the reconstructed points in the SPD layers  “tracklets” are found associating each point of the first layer to all the points of the second layer within a window Δφ of azimuthal angle.  Z vertex estimated as the mean value of the z i of intersections between the tracklets and the beam axis Vertex reconstruction procedures Using tracks  Vertex finding: first estimate of the vertex position using track pairs.The coordinates of the vertex are determined as:  Vertex fitting: tracks are propagated to the position estimated in the previous step and vertex position obtained with a fast fitting algorithm

6. Study of efficiency and resolution for ~8800 proton-proton collisions @ B=0.5 T Efficiency and resolution studied as a function of dN/dy, using the following bins: Study of vertex reconstruction performance 123456 dN/dy<5>5 & <7>7 & <12>12 & <15>15 & < 22>22

7. Efficiency = ratio of events with reconstructed vertex and total number of events Vertex reconstruction efficiency vtxSPD vtxTRK dN/dy vtxSPD: no vertex for n tracklets <=1 particles out of acceptance vtxTRK: no vertex for n tracks <2 lower efficiency because of selection of tracks (6 points in ITS, tracking requirements..)

8. Resolution (1) Z vtxSPD

9. Resolution (2) Z vtxSPD Z vtxTRK X vtxTRK Y vtxTRK dN/dy Resolution = RMS of the distribution Z measured -Z true

10. Resolution (3) Mean of the distribution Z measured -Z true

11. Tracking in the ITS (1) TOF TRD ITS TPC PHOS RICH Traking steps:  Seeding in the external pads of the TPC  Propagation trough the TPC (Kalman filter)  Prolongation of TPC tracks to the ITS and propagation through the ITS (Kalman filter)  ITS stand-alone tracking  Back propagation to TPC and TRD,TOF

12. Parallel tracking in the ITS(1) PPR II Prolongation to the ITS: more clusters assigned to a track (within a χ 2 window) choice of the most probable track candidate following: sum of χ 2, dead zones, dead channels, sharing of clusters.. Findable tracks: more than 60% of pad-row crossed in the TPC, all 6 layers crossed in the ITS

13. Parallel tracking in the ITS(2) Transverse momentum resolution

14. Stand-alone tracking in the ITS (1) expected  Use of vertex -> primary tracks For each couple of points of layer 1 and 2 in a ( ,  ) window the curvature of the “candidate track” is evaluated using the vertex information. The expected value of  on the next layer is evaluated and it is considered as center of the ( ,  ’) window on next layer. The precedure is repeated for all layers. Several loops increasing the window size and eliminating the points associated to found tracks.

15. Findable tracks: primaries with at least 5 points in the ITS Fake tracks: tracks with more than 1 wrong cluster Test on 6 hijing events (dN/dη=2000) and on ~8800 pp events @ B=0.5 T. Stand-alone tracking in the ITS (2) No improvement at low p T

16. Stand-alone tracking in the ITS (3) Tuning of φ and θ depending on multiplicity dN/dη=2000 Pt(GeV/c) larger improvement more fake tracks

17. Stand-alone tracking in the ITS (4)

18. PID in the ITS (1) Mesurement of the ionization energy loss in 4 layers (SDD,SSD).  p,k,π with 0.2<p<1.1 GeV/c  No e,μ because of overlaps in the ionization curves Particle identification based on the information coming from tracking Use of the 4 dE/dx signals (no truncated mean), combined PID (Bayesian probability)

19. PID in the ITS (2) The detector response functions are fitted with convolutions of a Gaussian and a Landau function  4 parameters: width and most probable value of the Landau distribution, and width and total area of the gaussian distribution Conditional probabilities density functions are obtained dividing the response functions by their area.

20. For each particle, the conditional probability density function for a vector of signals S is the product of the corresponding normalized response functions: The conditional probability is: The combined PID uses the Bayesian probability in order to get the probability of a track with a set of signals S of being of type i: with P(i) the prior probability for a particle i, i.e. the concentration of the different particle species on one set of events. Since it depends on the collision type and on the event selection, in this study we assumed P(p)=P(k)=P(π)=1/3, because we are interested in the performance of the PID algorithm in a model-independ way. PID in the ITS (3)

21.  300 central Pb-Pb events (0<b<5 fm), B=0.5 T Tracking in the ITS + back propagation to the TPC  6/6 clusters in the ITS The prior probabilities are estimated using tracks, assuming equal prior probabilities and, using the PID algorithm, counting the tracks tagged as type i in the momentum range p,p+Δp and taking the highest Bayesian probability among the 3 possibilities (p,K,π): Iteration of this procedure PID in the ITS (4)

22. Efficiency and contamination 1 iteration

23. Efficiency and contamination 4 iterations

24. Future plans Optimization of vertexer with tracks (A. Dainese) Optimization of stand-alone tracker in order to change window sizes and number of iterations dependin on multiplicity Study of multiplicity with stand- alone ITS (tracking+PID)