Vertex and track reconstruction with the ALICE Inner Tracking System

Transcription

1 Vertex and track reconstruction with the ALICE Inner Tracking System The 5th International Nordic "LHC and Beyond" Workshop St.Petersburg, 8-11 June 2010 Leonardo Milano Univ. & INFN, Torino for the ALICE Collaboration 1

2 OUTLINE INTRODUCTION The ALICE experiment at the LHC The ALICE Inner Tracking System Reconstruction and vertex determination in ALICE VERTEX WITH SPD TRACKLETS Efficiency of vertex determination Resolution of vertex determination Online monitoring of the beam position VERTEX WITH TRACKS Efficiency of vertex determination Resolution of vertex determination PILEUP Pileup tagging with Silicon Pixel Detector CONCLUSIONS 2

3 THE ALICE EXPERIMENT AT THE LHC STATUS PHOS: 3/5 EMCAL: 4/12 TRD: 7/18 Description of the ALICE detector in ALICE PPR Volume I or ALICE Technical Paper 3

4 THE ALICE INNER TRACKING SYSTEM - ITS 6 layer (2 of each type): SPD -> Silicon Pixel Detector SDD -> Silicon Drift Detector SSD -> Silicon Strip Detector Layer Det. Type Radius (cm) Length (cm) 1 SPD SPD 3 Resolution (μm) rφ Z SDD SDD SSD SSD Leonardo Milano - Univ. & INFN Turin SPD LHC and Beyond, St.Petersburg 8-11 June 2010 SSD SDD 4

5 THE ALICE INNER TRACKING SYSTEM ITS (II) Energy loss in TPC Energy loss in ITS 5

6 THE ALICE INNER TRACKING SYSTEM ITS (III) d0xy resolution [micron] d0xy resolution vs pt p0 = p1 = p2 = d0xy = [p0] + [p1]/pt^[p2] ALICE performance Work in progress p0 = p1 = p2 = MC sim Data 900 GeV pass Leonardo Milano - Francesco Prino pt [GeV/c] Impact parameter resolution with ITS standalone tracks SDD SDD 6

7 RECONSTRUCTION IN ALICE General reconstruction strategy : 1. First estimate of the vertex position from SPD (see next slides) 2. Reconstruction in the Time Projection Chamber TPC based on Kalman filter algorithm, from outside inward 3. Prolongation of TPC reconstructed track to ITS 4. Track back-propagation to outermost ITS layer, TPC and outer detectors 5. Re-fitting of reconstructed track inward 6. Accurate vertex determination using tracks (see next slides) SPD vertex determination is the starting point for the reconstruction!! Primary vertex resolution affects the impact parameter resolution : σ d 0(rϕ ) = σ track σ vertex SDD Selection of primary tracks Heavy flavour studies 7

8 RECONSTRUCTION IN ALICE (II) pp 7 TeV, pass1 reconstruction ITS prolongation efficiency ALICE work in progress 20/05/2010 at least 2 ITS hits (Data) at least 1 SPD hit (Data) at least 2 ITS hits (MC) at least 1 SPD hit (MC) ITS prolongation efficiency SDD p t [GeV/c] SDD 8

9 INTERACTION VERTEX WITH SPD SPDZ VERTEXER Only Z coordinate! Tracklet finding SPD tracklet = Iine segment built using clusters in the two %layer of SPD within a small azimuthal window (0.01 rad) Calculation of the distance of closest approach between tracklet and the nominal beam position Z i Layer 2 Layer 1 Definition of the Region Of Interest (ROI) around the peak of the Z i distribution Vertex position is the weighted mean in the ROI z mean = N i N i Z i σ i 1 σ i ROI Beam axis σ Vertex is the propagation of the error of the cluster SDD From MC simulation SPDZ requires the knowledge of the x and y beam coordinates with an accuracy 200 μm 9

10 INTERACTION VERTEX WITH SPD SPD3D VERTEXER 3D vertex reconstruction 1. Tracklet finding must cross a cylindrical fiducial region point in a given azimuthal window 2. Tracklet selection (DCA between pair of tracklets < 1mm) N 3. Vertex determination from 2 D = d i % the minimization of D: 1 d i = ( x x i 0 ) σ xi x ={X,Y,Z } Procedure repeated twice with different selection cuts SPD3D is the default algorithm From ALICE-INT If 3D reconstruction fails SPDZ is called to recover the event 10

11 INTERACTION VERTEX WITH TRACKS Three main steps: 1. Track selection Reject secondary tracks 2. Vertex Finding Give a first estimate of the vertex position 3. Vertex Fitting Give an optimal estimate of the vertex position Give covariance matrix of the vertex Give a χ 2 value (vertex quality) Beam position in the transverse plane can be used as constraint in the fit This method requires the full event reconstruction 11

12 DATA SAMPLES 900 GeV 900 GeV pp collisions - pass6 reconstruction MC Pythia D6T Tuning MC Phojet 2.36 TeV 2.36 TeV pp collisions pass4 reconstruction MC Pythia D6T Tuning MC Phojet 7 TeV 7 TeV pp collisions - pass1 reconstruction MC Pythia Perugia0 Tuning MC Phojet Efficiency : n_ev reconstructed/n_ev triggered 12

13 VERTEX WITH TRACKLETS 13

14 VERTEX WITH TRACKLETS - EFFICIENCY - 7 TeV Ev with SPD vertex / triggered ev SPD vertex efficiency vs ntrklets ALICE performance work in progress data_7tev (3D+Z reconstrucion) data_7tev (only 3D reconstruction) MC Perugia0 (3D+Z reconstrucion) MC Perugia0 (only 3D reconstruction) MC Phojet (3D+Z reconstrucion) MC Phojet (only 3D reconstruction) Phojet Pythia agreement Data MC agreement VertexerZ called in lowmultiplicity events SPD vertex reconstructed in 96 % of the events Tracklet multiplicity There are events with found vertex and 0 tracklets: different tracklet selection criteria in vertexer and trackleter 14

15 VERTEX WITH TRACKLETS - EFFICIENCY - ENERGY COMPARISON Ev with SPD vertex / triggered ev SPD vertex efficiency vs ntrklets ALICE performance work in progress data_0.9tev (3D+Z reconstrucion) data_0.9tev (only 3D reconstruction) data_2.36tev (3D+Z reconstrucion) data_2.36tev (only 3D reconstruction) data_7tev (3D+Z reconstrucion) data_7tev (only 3D reconstruction) Different energies agreement We are looking to a multiplicity dependence! Tracklet multiplicity Multiplicity Integrated 900 GeV 2.36 TeV 7 TeV 3D reconstruction 71 % 78 % 81 % 3D + Z reconstruction 93 % 97 % 96 % Data 15

16 VERTEX WITH TRACKLETS - EFFICIENCY - RUN DEPENDENCE SPD3D & Overall efficiency vs run ALICE performance work in progress Efficiency D/triggered 900 GeV V0 off in 2.36 TeV runs, different triggered sample D+Z/triggered 900 GeV 3D/triggered 2.36 TeV 3D+Z/triggered 2.36 TeV 3D/triggered 7 TeV 3D+Z/triggered 7 TeV Trigger: 900 GeV and 7 TeV : SPD & V TeV : only SPD run 900 GeV 2.36 TeV 7 TeV N SPD0cls / N triggered

17 VERTEX WITH TRACKLETS - EFFICIENCY - Zreco DEPENDENCE SPD3D/reconstructed SPD3D/reconstruted vs vs Z reco Z ALICE performance work in progress 3D rec / (3D+Z rec) TeV 2.36 TeV 900 GeV data_0.9tev Phojet 900 GeV Pythia D6T 900 GeV data_2.36tev Phojet 2.36 TeV Pythia D6T 2.36 TeV data 7 TeV Phojet 7 TeV Pythia Perugia0 7 TeV Z [cm] 3D/(3D+Z) becomes larger with increasing energy Phojet closer to data at all energies 17

18 VERTEX WITH TRACKLETS RESOLUTION Vertex Spread Method (VSM) Sigma of vertex distributions in bins of tracklet multiplicity convolution of: - Resolution of the SPD Vertexer - Luminous region dimension fit performed with: f (ntracklets) = σ D + α ntracklets β Diamond extracted from ntrklets --> σ D estrimated from tracks Better estimation of diamond size from tracks (see next slide) α TRK α SPD / 2 18

19 SPD vs TRK VERTEX - VSM RESOLUTION - 7 TeV TRK-SPD vertex distrib sigma [!m] Vertex spread distribution at x SPD y SPD x TRK y TRK f(n ) = D tracklets DATA parameters- X TRK = 34 " 2 DX = 518 " 11 = 1.39 " 0.04 s = 7 TeV, * = 2m DATA parameters - Y TRK = 33 " Tracklet Multiplicity n tracklets DY ALICE Performance = 542 " 12 = 1.40 " 0.04 β* is the amplitude function Estimation of luminous region from tracks!!! 19

20 VERTEX WITH TRACKLETS - VSM RESOLUTION GeV SPD Vertex spread distribution at s = 0.9 TeV SPD vertex distrib sigma [!m] x MC y MC x DATA y DATA f(n ) = D tracklets DATA fit parameters - X DX = 200 fixed = 1075 " 30 = 1.02 " 0.03 n tracklets ALICE Performance DATA fit parameters - Y = 220 fixed DY = 1021 " 28 = 0.91 " Tracklet Multiplicity Asymmetric luminous region Expected resolution from MC (ntracklets=35) 150 μm Same order of the beam spot 20

21 VERTEX WITH TRACKLETS - VSM RESOLUTION TeV SPD Vertex spread distribution at s = 2.36 TeV SPD vertex distrib sigma [!m] x MC y MC x DATA y DATA DATA fit parameters - X DX f(n ) = D tracklets = 70 fixed = 1045 = 0.99 " 46 " 0.04 DATA fit parameters - Y = 75 fixed Tracklet Multiplicity n tracklets DY = 1112 " 44 = 1.05 ALICE Performance " 0.03 Expected resolution from MC (ntracklets=50) 100 μm We are dominated by the vertexer resolution 21

22 VERTEX WITH TRACKLETS - VSM RESOLUTION - 7 TeV SPD Vertex spread distribution at s = 7 TeV, * = 9m SPD vertex distrib sigma [!m] x MC y MC x DATA y DATA f(n ) = D tracklets DATA fit parameters - X DX = 50 fixed = 1089 " 25 = 1.10 " 0.02 n tracklets ALICE Performance DATA fit parameters - Y DY = 75 fixed = 1115 " 25 = 1.12 " Tracklet Multiplicity Expected resolution from MC (ntracklets=100) 90 μm Still dominated by the vertexer resolution 22

23 VERTEX WITH TRACKLETS RESOLUTION Half Event Method (HEM) 2 estimations of the interaction vertex: using recpoints in the even/odd sectors of the SPD gaussian fit of x even x odd in bins of number of contributors (i.e. tracklet used in the reconstruction) Comparison with resolution obtained from MC using x reco x gen distribution No informations on the size of the beam spot Z resolution estimation 23

24 VERTEX WITH TRACKLETS HEM PULLS Sigma of standardized distribution of residuals x even x odd σ x,even 2 +σ x,odd 2 24

25 VERTEX WITH TRACKLETS Detector Algorithm Same algorithm used offline can be used for a quasi-online reconstruction --> monitoring of the beam position Vx Runs DA Vy Runs DA Offline Online x SPD (cm) y SPD (cm) Why SPD? Vertex determination w/o tracking Closest detector to the interaction point good resolution in the transverse plane Run Number Run Number 25

26 VERTEX WITH TRACKS Leonardo Milano - Univ. & INFN Turin LHC and Beyond, St.Petersburg 8-11 June

27 VERTEX WITH TRACKS 900 GeV EFFICIENCY Ev. with TRK vertex / triggered ev TRK efficiency vs ntrklets ALICE performance work in progress data_0.9tev (with constrain) Phojet 900 GeV (with constrain) Pythia 900 GeV (with constrain) data_0.9tev (w/o constrain) Phojet 900 GeV (w/o constrain) Pythia 900 GeV (w/o constrain) Phojet Pythia agreement Data MC agreement The use of constraint enhance the efficiency, especially in low multiplicity events Tracklet multiplicity Remark: constraint means that the beam position in the transverse plane is used as constraint in the vertex fitting TPC off in 2.36 TeV runs 27

28 VERTEX WITH TRACKS 7 TeV EFFICIENCY Ev. with TRK vertex / triggered ev TRK efficiency vs ntrklets ALICE performance work in progress data_7tev (w/o constraint) MC_7TeV_Phojet (w/o constraint) MC_7TeV_Perugia0 (w/o constraint) Tracklet multiplicity Overall efficiency 900 GeV 7 TeV TRK rec with constraint 89 % / TRK rec w/o constraint 78 % 85 % Constraint will be implemented in pass2 reconstruction 28

29 VERTEX WITH TRACKS - VSM RESOLUTION 900 GeV Vertex Spread Method --> same used for tracklets Vertex spread distribution at s = 0.9 TeV TRK vertex distrib sigma [!m] x MC y MC x DATA y DATA f(n ) = D tracklets DATA fit parameters - X = 166 " 18 DX = 540 " 18 = 0.9 " 0.1 DATA fit parameters - Y = 200 " n tracklets DY ALICE Performance = 539 " 13 = 0.83 " 0.03 Tracklet Multiplicity Asymmetric luminous region 29

30 VERTEX WITH TRACKS - VSM RESOLUTION 7 TeV TRK vertex distrib sigma [!m] Vertex spread distribution at x MC y MC x DATA y DATA f(n ) = D tracklets DATA fit parameters - X = 51 " 3 DX = 474 = 1.24 " 13 " 0.05 s = 7 TeV, * = 10m DATA fit parameters - Y = 74 " n tracklets ALICE Performance DY = 503 " 15 = 1.3 " 0.1 Tracklet Multiplicity 30

31 VERTEX WITH TRACKS - RESOLUTION 7 TeV BEAM CONSTRAINT Vertex spread distribution at s = 7 TeV, * = 10m (MC) TRK resolution [!m] 2 10 x TRK y TRK x TRK with const. y TRK with const. ALICE Performance Tracklet Multiplicity 31

32 TRK vertex distrib sigma [!m] VERTEX WITH TRACKS - RESOLUTION 7 TeV Half Event Method Half Events Method splitting of the event with random combinations of track Half event vertex distribution at 700 x MC z MC x DATA z DATA f(n ) = tracklets DATA fit parameters - X K = -29 " 7 = 871 " 40 = 1.9 " 0.1 n tracklets K ALICE Performance DATA fit parameters - Z K = -40 " 11 = 601 " 25 = 1.44 " s = 7 TeV, * = 10m Tracklet Multiplicity 32

33 VERTEX WITH TRACKS HEM PULLS 7 TeV Pulls vertex TRK Sigma of standardized distribution of residuals Half event pulls distribution at x even x odd σ x,even 2 +σ x,odd 2 s = 7 TeV, * = 10m x HE y HE z HE Tracklets Multiplicity 33

34 VERTEX WITH TRACKS RESOLUTION VPM vs HEM, 7 TeV Comparison of the two method 34

35 PILEUP 35

36 PILEUP DETECTION SPD vertexer can be used to tag pile-up events Pileup event in SPD = superposition of indipendent pp collisions with Δτ pile < 100 ns Pileup in SPD can be caused by pp collisions occuring in: the same bunch crossing different bunch crossing SPD recpoints which are not been used in the primary vertex reconstruction can be used to check if there are other interaction vertices First vertex is the one with higher multiplicity 36

37 PILEUP RATE Comparison between the pileup fraction expected and the measured one. 7 TeV, β* = 9m 7 TeV, β* = 2m run Run GeV 7 TeV, β* = 9m 7 TeV, β* = 2m Pileup rate

38 CONCLUSIONS Two different tools for the vertex reconstruction with good efficiency Vertex with SPD tracklets without event reconstruction quasi-online monitoring of the beam position Vertex with Tracks accurate measurement of the interaction vertex estimation of the luminous region dimension Good agreement between data and MC Good understanding of systematics Moreover the SPD algorithm allows the tagging of pileup events on the basis of multiple vertices Example of the importance of the vertex determination in the charm analysis 38

39 THANKS! 39

40 Backup 40

41 VERTEX WITH TRACKS VERTEX FINDER AIM : give a first estimate of the vertex position in (x,y) to be used as a %starting point for the vertex fiter 3 different algorithm available: falgo = 1 or 2, approximation of tracks as straight lines. Calculation of the vertex position as the minimum distance between tracks at once, with or w/o weighting the tracks falgo = 3, tracks treated as helices. Calculation of the vertex position as the average among DCA points of all possible pairs of tracks falgo = 4 or 5, tracks treated as straight lines. Same calculation of the falgo =3 for the vertex position, with or w/o weighting the tracks Default algorithm is falgo = 1 41

42 Fast Vertex Fitting, CMS Note 1997/051 VERTEX WITH TRACKS VERTEX FITTER Tracks are propagated to the point given by the vertex finder Each tracks gives an independent measurement of the vertex position : a χ 2 is calculated as the sum of the single track χ 2 w.r.t. a generic vertex r vtx : where r i is the (x,y,z) position of the i th track and V i = W i -1 is the cov matrix of r i, extracted from the track s cov matrix The solution that minimizes this χ 2 is analytic: vertex vertex covariance matrix 42

43 AMPLITUDE FUNCTION Multiplicity Integrated 900 GeV 2.36 TeV 7 TeV ε x,y 3.75 μm 3.75 μm 3.75 μm 3.75 μm β* 10 m 10 m 10 m 2 m σ diamond, xy (nominal) 198 μm 120 μm 70 μm 30 μm 43

44 SPD VERTEX RESOLUTION Half Event Method MC check 44

45 SPD VERTEX PULLS - Half Event Method MC check 45

46 VERTEX WITH TRACKS - RESOLUTION 7 TeV AMPLITUDE COMPARISON TRK vertex distrib sigma [!m] Vertex spread distribution at x *= 10m y *= 10m x *= 2m y *= 2m f(n ) = D tracklets DATA fit parameters - X = 34 " 2 DX = 518 " 11 = 1.39 " 0.04 DATA fit parameters - Y = 33 " n tracklets s = 7 TeV DY ALICE Performance = 542 " 12 = 1.40 " 0.04 Tracklet Multiplicity 46

47 PILEUP in SPD Selection cuts on tracklets On recpoints not used in the first vertex determination tracklet with DCA < 1mm to the main vertex are removed 47

48 PILEUP DETECTION SELECTION ON PILE UP VERTEX Selections at the analysis level can be perfomed in order to obtain a good tagging efficiency togheter with a small number of false positives False positive : event with single vertex interaction wrongly flagged as pileup by the pileup tagging algorithm Transverse coordinate of pileup vertex inside the diamond (within errors) selection on the minimal Z distance between the two vertices Phojet 7 TeV selection on ncontribpile = number of tracklets used in the pileup vertex reconstruction 48

49 SELECTION CUT ON PILEUP Selection on the transverse plane Selection on the distance "x stan d = x pile # x beam $ 2 x,pile +$ 2 x,beam Event tagged as pileup if: %Δz > Δz min (Δz min 0.8 cm)! "y stan d = y pile # y beam $ 2 y,pile +$ 2 y,beam Selection on ncontribpile! Event tagged as pileup if: %Δx stand < 2 and Δy stand < 2 Event tagged as pileup if: ncontribpile > ncontribpile min More details from ALICE Internal Note ALICE-INT February 4,

50 PILEUP RATE Comparison between the pileup rate expected and the measured one. Expected pile up ratio Calculation based on rate of good interaction Poisson statistics (Thanks to F.Antinori) 7 TeV, β* = 9m Raw measurement of pileup ratio corrected for: efficiency of reconstruction selection on the pileup vertex characteristics false positive good agreement between estimated and expected rate ncontribpile dependence almost flat: small false positive contamination good estimation of correction factors run

51 EXPECTED PILEUP RATE Expected pileup ratio can be calcutated starting from: R corr = number of good interaction / second estimated from number of events in various trigger classes P trig = R corr / number of bunch crossing per second P trig = can be interpreted as the probability of having at least one good pp in a bunch crossing P(n >= 1) = P trig The value of μ can be calculated from the relation: P(0;μ) = 1 P(n >= 1) = 1- P trig Hence the probability of pileup event can be calculated as P pile = P(n >= 2) = 1 P(0) P(1) 51

52 PILEUP RATE MULTIPLICITY DEPENDENCE 7 TeV MC 52