adam jacholkowski catania & cern 1 24-25/01/05 gsi silicon tracking at wa97 and na57...

Adam JacholkowskiCatania & CERN 1

24-25/01/05 GSI

Silicon Tracking at WA97 and NA57

• introduction to WA97/NA57 : setup & physics

• silicon pixels – hardware aspects • alignment• pattern recognition & track fit• vertex finding

• summary & final remarks


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The NA57 Experiment

Study of the dependence of hyperon enhancements on:

WA97 p-Be sample used as reference data at 158 A GeV.

Data samples:

• Interaction volume - centrality down to Nwound ~ 50• Collision energy - data at two beam momenta - 158 and 40 A GeV/c

INTRODUCTION(1)

3-4 Tb in CASTOR (mass storage)

System Beam energy Sample size Data taking year

Pb-Pb 158 A GeV 230+230 x 106 evts 1998+2000

Pb-Pb 40 A GeV 240 x 106 evts 1999

p-Be* 40 A GeV 60+110 x 106 evts 1999+2001

(continuation and extension of WA97)


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INTRODUCTION

Example:

INCREASES WITHSTRANGENESSCONTENT !

systematic error

NA57 – example of the enhancement study

result: for more see G. Bruno plenary talk at QM2004

Bepwound

cent

PbPbwound

cent

NY

NY

Enhanc

.

INTRODUCTION(2)

Enhancement def.

central rapidity

(one unit) yield

statistical error


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WA97 (predecessor of NA57) set-up in the OMEGA magnet

Target: Be, Pb; Beam: p, Pb, 158 A GeV/c

momentum Magnetic field: 1.8 T Silicon telescope: tracking

device (7 pixel and 10 microstrip planes, 5cm x 5cm)

Pad chambers: lever arm Scintillation petals: lead run

centrality trigger (40% inel)

Multiplicity detectors: off-line event centrality analysis

d

L

L = 30 cmd = 60 cm (Pb-Pb), 90 cm (p-A) = 40 mrad (Pb-Pb), 48 mrad (p-A)

(~ 0.5 M pixels)


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B=0 event


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NA57 SETUP (Pb - Pb run)

1.4 T

Target: 1% Pb

Scintillator

Petals: centrality trigger

MSD: multiplicity silicon detector

Tracking device:

silicon pixel planes

(5 x 5 cm2 cross section)

Lever arm: double side strips

5 cm X

(~ 1.0 M pixels)


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Ω3 pixel (single) card

Ω2 pixel plane (box)

5 cm


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Single pixel cell of the LHC1/Ω3 chip

LHC1: A semiconductor pixel detector readout chip with internal,

tunable delay providing a binary pattern of selected events

Erik H. M. Heijne et al

Nucl. Instr. & Methods A 383 (1996) 55

( RD19 & WA97 collaboration)


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Pixel Maps (full planes) 1

as seen by the

beam (along X)

(98256 pixel cells)Z

Y

Ω3Y

double length

pixels

(chip border)

10 000 events


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Pixel Maps (full planes) 2

(73656 pixel cells)

only one card switched ON

Ω2Z

10 000 events


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Dead Time (1)

LDC – Local

Data Collector(group of pixel cards)

ms

DT – proportional to amount of fired pixels (hits + noise)


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Dead Time (2)

Risk of saturating DAQ

in case of high level of noise, but

exaggerated noise suppression

lowering planes efficiency

compromise

Importance of masking

noisy pixels and (chips)

efficiency monitoring

Data Base


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MAIN ANALYSIS STEPS

• ALIGNMENT and CALIBRATION DATA BASE

• GEOMETRICAL RECONSTRUCTION clusters, tracks• V0 FINDING Λ and K0 candidates

• CASCADE RECONSTRUCTION (V0s + tracks) • PARTICLE SIGNALS EXTRACTION (selection cuts) Gold-Plated ntuples• CORRECTING for ACCEPTANCE and LOSSES EVENT-BY- EVENT WEIGHTING (and/or de-convolution)

• EXTRAPOLATION TO FULL Pt AND ONE UNIT OF rapidity• NORMALIZATION (beam flux,target) YIELDS• MULTIPLICITY RECONSTRUCTION CENTRALITY


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ALIGNMENT(1)

Starting point – optical bench survey measurements + internal pixel ladder positions (known from construction)

Internal pixel alignment cross checks using strips tracks (WA97) and exploiting ladder overlaps

Transverse & longitudinal alignment using straight tracks (special B=0 and telescope in proton beam runs)

Small correction tilt angles relative to the telescope axis Cross-alignment of the Z and Y planes Alignment data taken periodically and/or after each

intervention on the optical bench ( results stored in the DB)


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Alignment (2)

Single Y (vertical) ladder Y-plane tilt test

mm

Z

Z

Y

mic

rons


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Parabolic Approximation (used in Pat. Rec.)

310

circle

parabcircle

y

yy

xtgzz

xxtgyy

0

20 2

1

circle

parabola

Y

X

31 cm

Example:

p = 2 GeV/c

φ = 0.

ρ = 5m

cmy

cmy

parab

circle

000.1

,001.1

Sagitta = L2 /8ρ= ¼ cm

(50 pixels)

10 μ diff.


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Polynomial Parameterization

Fit (example)


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TRACK RECONSTRUCTION PRECISION

measNpts

pts

measNpts p

Np

XpK

termsdiagonalnon

LK

L

LKL

pL

K

LBp

322

0

3

2

2

2

202

22

202

22

4

2

4

20

22

)/1(4

7)/1(

10015.0

,

cos6

1

)cos(

26

3

1

)cos(

26

tan

cos3

4

)cos(

96

0003.0

cos)/1(

(3 points parabolic approximation)

B in kGs, p in GeV/c, L in cm

σ0 = pitch/sqrt(12)

R.L. GLUCKSTERN

Nucl. Instr. & Methods 24(1963) 381


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NA57 case : λ ≈ 0, L ≈ 30cm, B ≈ 14kGs,X0 ≈ 30cm/(9x 0.012) = 277cm, pitch = 50μm

)/(0.2)(,25.0)(

/045.0)/(,0037.0)/(6

126

3

496

)0003.0(

1)/1(

22

202

2

4

20

22

pmradmrad

ppppp

LKL

L

K

LBp

mscmeas

mscmeas

Δp/p meas and msc errors equal at p ≈ 12.2 GeV/c (4.5%)

p = 12.2 Δφ = 0.25 & 0.17mrad → 0.3 mrad

meas msc.


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Pattern Recognition(1)

Parabolic track model (very good approximation!) in the bending plane

Starting from 3 points (e.g. in the first and the last plane + in one of the intermediate planes) then adding other points lying within the predetermined limits relatively to the predictions

Constraints: Npoints ≥ Nmin (for example 6-7 out of 9-11 possible) with a requirement of a minimum number of points in each type of pixels (Z or Y-like)

Semi-combinatorial,

using predefined plane configurations of the compact part only


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-)

Pattern Recognition(2)

xx

x


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Pattern Recognition (3)

2D - PR+ matching in WA97, 3D - PR in NA57 Hit sharing level controlled according to the chosen

tolerance: ambiguities resolved on the basis of χ2

Track finding efficiency bigger than 95%, while Kalman Filter ε ≈ 50% ! ( sparse points + multiple scattering)

Ghosts kept at a negligible level (below 1% ) PR optimization - multiplicity dependent (different in

Pb-Pb and p-Be)


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Track Fit(Quintic Spline)

pzydx

dzz

dx

dyy

zBzyByBzypz

yBzyBzBzypy

yzx

zyx

/1,tan,tan,,

,

)1()1(

)1()1(

00

''

2''''2/12'2'''

2'''2/12'2'''

H. Wind

Nucl. Instr. & Methods 115 (1974) 431


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ORHION – reconstruction programme

Fortran code developed under Patchy: new versions kept backward compatible (useful for reprocessing !)

Working both on real and simulated (GEANT MC) data Internally split into different (main) sub processes: OR – steering ST – pattern recognition

TF – track fit XC – lever arm track improvement

V0 – secondary vertices finder DST-output files of different formats input for the

analysis programs


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p-Be 40 GeV/c

Ξ event

[cm]

Y

X

Ω3YΩ3ZΩ2YΩ2ZΩ3YΩ2YΩ2ZΩ2Y Ω2Z Ω3YΩ3Y planes sequence

aspect ratio ≈ 9 !


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p-Be 40 GeV/c

Ξ event

[cm]

Z

X

Ω3YΩ3ZΩ2YΩ2ZΩ3YΩ2YΩ2ZΩ2Y Ω2Z Ω3YΩ3Y planes sequence

aspect ratio ≈ 9 !


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Vertex Finding

Primary vertex – from secondary tracks extrapolation (the μ-strips beam telescope used only in the WA97 p-Be run ) : - event-by-event (WA97) or

- run-by-run (to handle more peripheral collisions in NA57) V0 finding – pairs of oppositely charged tracks extrapolated

first to a ref. plane, then search for the point of nearest approach using helix parameterization

The nearest approach distance – a crucial parameter in selecting clean signals (removing background):

a typical cut value dmax/2 (alias close) = 0.04 cm


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HYPERON DETECTION

30 cm

5 cm

5 cm

by

by

Plus many other associated tracks

Each hyperon (particle) assigned to

a centrality class according to MSD Ncharged

X


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Mass Resolution: Ξ

158 A GeV/c 40 A GeV/c


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Mass Resolution: K0s

158 A GeV/c 40 A GeV/c

FWHM = 24 MeVFWHM = 16 MeV


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Summary

WA97 first application of pixel technology (Ω2 then Ω3/LHC1 chips) in conjunction with strips

NA57 pattern recognition and tracking entirely based on pixel detectors

WA97 and NA57 experience pixel detectors – a powerful tool for high precision tracking (3D)


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Technology still in rapid evolution,

future CERN LHC experiments/NA60

Final Remarks (1)


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Message to CBM

Final Remarks (2)

PIXEL TECHNOLOGY

is a powerful tool for physics

once good care is taken of all necessary

elements of hardware (calibration) and

software (alignment, noise and efficiency control)

environment


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Physics Department, University of Athens, Greece; Dipartimento IA di Fisica dell'Università e del Politecnico di Bari and INFN, Bari, Italy; Fysisk Institutt , Universitetet i Bergen, Bergen, Norway ; Høgskolen i Bergen, Bergen, Norway; University of Birmingham, Birmingham, UK; Comenius

University, Bratislava, Slovakia; University of Catania and INFN, Catania, Italy; CERN, European Laboratory for Particle Physics, Geneva,

Switzerland; Institute of Experimental Physics Slovak Academy of Science, Kosice, Slovakia; P.J. Safárik University, Kosice, Slovakia; Fysisk

institutt, Universitetet i Oslo, Oslo, Norway; University of Padua and INFN, Padua, Italy; Collège de France, Paris, France; Institute of Physics,

Prague, Czech Republic; University “La Sapienza'' and INFN, Rome, Italy; Dipartimento di Scienze Fisiche “E.R. Caianiello'' dell'Università and INFN, Salerno, Italy; State University of St. Petersburg, St. Petersburg, Russia; IReS/ULP, Strasbourg, France; Utrecht University and NIKHEF,

Utrecht, The Netherlands.

THE NA57 COLLABORATION


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CENTRAL RAPIDITY YIELD MEASUREMENT

5.0

5.0

2

0 dd

ddd

CM

CM

y

y m TTcent

ym

NmyY

ONE UNIT OF RAPIDITY

FULL Pt RANGE

INTRODUCTION

T from Max-Log-Likelihood


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Hyperon reconstructionΛ(cowboy) acceptance

cmBq

pd cms 4.37

2max

-

d

Δ

cmBq

pd cms 4.37

3

2max

cm9.0)10,(


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Pixel maps (single cards)

Importance

of masking

noisy pixels

and (chips)

efficiency

monitoring

Data Base(36828 pixel cells) (49128 pixel cells)


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EXAMPLE OF ORHION PROCESSING(Pb-Pb 2000 Background)

• Background data – representative sample of all data (each 200th event – 5%), 24 files• Parallel running at CERN (Linux Batch) overall 1 day and 1 night human time• About 3-4 NCU hours per file

1440-1920 NCU hours of full 2000 data ( 230 Mevts ) ORHION processing (distributed between all labs !!)

Slightly less for Pb-Pb at 40 GeV/c, then still less for p-Be (one week)


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Selection of Hyperons (and KS0)

X-Ξ vertexcloseΛ impact

Cleaning of the signals via geometrical cuts


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WEIGHTING PARTICLES

• A weight is associated with each selected particle to

correct for acceptance, efficiencies and cuts ( ~few thousands)

BRN

Nweight

rec

gen 12

2 different selections (cuts)

spread


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WEIGHTING PROCEDURE (2)

• Weights are calculated by Monte Carlo:

- generated hyperons (Ngen) are traced through a

GEANT simulation of the NA57 apparatus

- track hits are merged with true events

- resulting events are processed through the

reconstruction and analysis chain

- reconstructed hyperons are counted (Nrec)

Simulation thoroughly checked against real data


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WEIGHT STATISTICS at 158 GeV/c

_

0sKParticle

weighted

collected

3340 2350 2718 6444 936 432 192

x400 x400 x50 x1 x1 x1 x1

• Most expensive cascade particles:1-3 NCU hours on LXPLUS, 10000 Λs and 10000 K0s about 40K NCU hours, alias 1 working month (estimate)

• 2001 p-Be data weighting ( 4000 Λs only) just in one week at CERN


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DECONVOLUTION

An alternative method to weighting

(which is precise but CPU expensive),

applicable to high statistics samples

F. Antinori et al

Transverse mass spectra of strange and multi-strange

particles in Pb-Pb collisions at 158 A GeV/c

Eur. Phys. J. C 14, 633-641 (2000)

gAf 1ˆ


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Energy multiplicity dependence

logarithmic scaling

adam jacholkowski catania & cern 1 24-25/01/05 gsi silicon tracking at wa97 and na57...

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