Multiplicity analysis and dN/d h reconstruction with the silicon pixel detector

Multiplicity analysis Multiplicity analysis and and

dN/ddN/dreconstructionreconstructionwith the with the

silicon pixel detectorsilicon pixel detector

Terzo Convegno Nazionale sulla Fisica di ALICETerzo Convegno Nazionale sulla Fisica di ALICEFrascati (Italy) – November 12-14, 2007Frascati (Italy) – November 12-14, 2007

Maria Nicassio Maria Nicassio (Univ. and INFN Bari)(Univ. and INFN Bari)

in collaboration within collaboration with

D. Elia, B. Ghidini (Univ. and INFN Bari)D. Elia, B. Ghidini (Univ. and INFN Bari)T. Virgili (Univ. Salerno)T. Virgili (Univ. Salerno)

Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12, 2007

ContentsContents

Introduction: physics motivation tracklet reconstruction algorithm

Status of the analysis: study of the corrections:

geometrical acceptance detector efficiency background from secondaries vertex reconstruction efficiency minimum bias trigger acceptance

Summary and outlook

IntroductionIntroduction

Why multiplicity: first measurement in p-p collisions for ALICE global observable characterizing the event comparison with results obtained at lower energies

Why multiplicity with pixels: available in a short time advantages over reconstructed tracks (ITS+TPC)

larger acceptance coverage only alignment of the two pixel layers required

Acceptance coverage:

SPD layers:-2.0 < < 2.0 (inner)

-1.5 < < 1.5 (outer)

Multiplicity reconstruction: (a) counting clusters on the inner layer (|| < 2.0)

no detector alignment required

reliable at high multiplicity

(b) counting tracklets (|| < 1.5) alignment, vertex needed

more reliable (e.g. good noise rejection)

Pseudorapidity reconstruction: vertex needed for both methods the angle of the cluster on the inner layer is used

Fiducial window

dN/ddN/d distributions distributions (uncorrected)(uncorrected)

asymmetry due to the detector efficiency losses in PDC06

Inner layer clusters Tracklets

Corrections: SPD acceptance Corrections: SPD acceptance (I) (I)

MC data samples: Pb-Pb (HijingParam) collisions @ 5.5 TeV:

20,000 tracks/evt, within [-4,4]

event vertex-Z within [-20,20] cm

fully efficient SPD 2,500 evts pure

geom acceptance

standard PDC06 SPD dead chip map 2,500 evts

convoluted acc+eff

Correction matrix:

binning and range: within [-3,3] nEtabins = 120 d = 0.05

vtx-Z within [-20,20] cm nVtxzbinx = 40 dVtx-Z = 1 cm

Calculation method: detectable_tracks (fDenAcc):

primary charged

no decay, no secondary interactions up to the sensitive layer

detected_tracks (fNumAcc): detectable tracks with associated (label) cluster on the sensitive

if there are 2 clusters on adjacent modules: track is counted twice

this takes into account the overlapping regions

compute acceptance and error in each bin

(fAcc,fErrAcc)

statistics in each bin: detectable tracks: 104

resulting error on the acceptance: 10-3

Corrections: SPD acceptance Corrections: SPD acceptance (II) (II)

Results: convoluted acceptance & efficiency:

Tracklets

Inner layer

Outer layer

Corrections: SPD acceptance Corrections: SPD acceptance (III) (III)

Correction applied:

Corrections: Corrections: acceptance & efficiency (I)acceptance & efficiency (I)

Corrections: Corrections: acceptance & efficiency (II)acceptance & efficiency (II)

Correction applied:

Corrections: Corrections: background from secondaries background from secondaries (I)(I)

Studied using the SPD cluster labels

Definition of background:

for clusters on the inner layer:

clusters having secondary track labels only

for tracklets:

at least one of the two clusters in the

tracklet

having secondary track labels only

Clusters (inner layer):

Corrections: Corrections: background from secondaries background from secondaries (II)(II)

Clusters (inner layer):

correction(to be multiplied by)

Corrections: Corrections: background from secondaries background from secondaries (III)(III)

Tracklets:

Tracklets from primaries

Tracklets from secondaries

Tr(P+P)

Tr(P+P’)

Tr(P+P)+Tr(P+P’)

Tr(S+S) + Tr(P+S)

Tr(S+S) Tr(P+S)

to be subtracted

P, P’ = cluster with a label of a primary track

S = cluster with all labels of secondary tracks

(total bkg fraction: 7.5%)

Corrections: Corrections: background from secondaries background from secondaries (IV)(IV)

Tracklets:

correction(to be multiplied by)

Corrections: Corrections: background from secondaries background from secondaries (V)(V)

Corrections: Corrections: vertex reconstruction (I)vertex reconstruction (I)

Generated dNch/d

N.B. The correction is integrated, but it should be a function of multiplicity and vertex position

Correction (to be multiplied by)

Corrections: Corrections: vertex reconstruction (III)vertex reconstruction (III)

The correction depends both on and on multiplicity at low multiplicityTo be checked as a function of Z-vtx

All corrections applied: inner layer clusters

Final dN/dFinal dN/d distributions distributions

All corrections applied: tracklets

Final dN/dFinal dN/d distributions distributions

Multiplicity distributions Multiplicity distributions (uncorrected)(uncorrected)

Multiplicity distributionsMultiplicity distributions

Background correction:

16% 7%

Background fractionsfor each event

Multiplicity distributionsMultiplicity distributions

All corrections applied:

MB2 =(GFO.and.V0OR).and.notBG

triggerwithEvents

eventsAllCorrection

Effect of trigger selection: Effect of trigger selection: first look (I)first look (I)

Trigger correction:

Effect of trigger selection: Effect of trigger selection: first look (II)first look (II)

Generated dN/d:MB2 =(GFO.and.V0OR).and.notBG

All events

No triggerNo vertex

vertextriggerwithEvents

eventsAllCorrection

Summary and outlookSummary and outlook

Multiplicity and pseudorapidity density in p-p: first measurement in ALICE reconstruction with the Silicon Pixel Detector only

Status of the analysis: raw reconstructed distributions with PDC06 data study of the main corrections:

acceptance, efficiency, background from secondaries, vertex, trigger

What next: check correction dependence on multiplicity, Z-vtx estimate of the systematics tests with PDC07 data

Backup slidesBackup slides

Couples of clusters associated with the same

Tracklet algorithm efficiency in p-pTracklet algorithm efficiency in p-p

Using the default cuts the algorithm efficiency is 99%

Tracklet algorithm efficiency in p-pTracklet algorithm efficiency in p-p

Couples of clusters associated with the same

Multiplicity analysis and dN/d h reconstruction with the silicon pixel detector

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