Tracking, PID and primary vertex reconstruction in the ITS

Elisabetta Crescio-INFN Torino

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)

Weakly decaying beauty and charm states

µm 21c MeV 2698m )(

µm 34c MeV 2472m )(

µm 132c MeV 2466m )(

µm 60c MeV 2285m )(

µm 147c MeV 1968m )(

µm 123c MeV 1865m )(

µm 312c MeV 1869m )(

0

0

0

ssc

dsc

usc

udc

scD

ucD

dcD

c

c

c

c

s

µm 368c MeV 5624m )(

µm 200001c GeV 6.4 m )(

µm 438c MeV 5370m )(

µm 460c MeV 5279m )(

µm 501c MeV 5279m )(

0

0

0

udb

bcB

bsB

bdB

buB

b

c

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Need for high precision vertex detector tracks from heavy flavour weak decays are typically

displaced from primary vertex by ~ 100’s µm

primary vertexprimary vertex

decay vertexdecay vertex

decay length = Ldecay length = L

track impact parametertrack impact parameter

Vertex reconstruction in the ITS (1)

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 zvertex (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)

*

2/ ,,,,,,,,

zyxbunchzyx

bunchzyx

vertzyx

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.

Zvertex estimated as the mean value of the zi 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

ij

ijpairs

found xN

x1

ijij

pairsfound y

Ny

1 ij

ijpairs

found zN

z1

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

1 2 3 4 5 6

dN/dy <5 >5 & <7

>7 & <12

>12 & <15

>15 & < 22

>22

Efficiency = ratio of events with reconstructed vertex and total number of events

Vertex reconstruction efficiency

vtxSPD

vtxTRK

dN/dy

vtxSPD: no vertex for ntracklets<=1

particles out of acceptance

vtxTRK: no vertex for ntracks<2

lower efficiency because of selection of tracks (6 points in ITS, tracking requirements..)

Resolution (1)

Z vtxSPD

Resolution (2)

Z vtxSPD

Z vtxTRK

X vtxTRKY vtxTRK

dN/dy

Resolution = RMS of the distribution

Zmeasured-Ztrue

Resolution (3)

Mean of the distribution

Zmeasured-Ztrue

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

Parallel tracking in the ITS(1)

PPR II

Prolongation to the ITS:

• more clusters assigned to a track (within a χ2 window)

• choice of the most probable track candidate following: sum of χ2, 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

Parallel tracking in the ITS(2)

Transverse momentum resolution

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.

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 pT

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

Stand-alone tracking in the ITS (4)

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)

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.

)(sfi

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)

SDDSSD

Ni isfiSR,

)|()|(

21...)|()|( ssdsdd dsdsiSRiSP

,,

)()|(

)()|()|(

Kpt

tPtSR

iPiSRsiP

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)

,,

)(

)()(

Kpt

tN

iNiP

Efficiency and contamination

1 iteration

Efficiency and contamination

4 iterations

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)