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Multiplicity analysis and dN/d  reconstruction with the silicon pixel detector Terzo Convegno Nazionale sulla Fisica di ALICE Frascati (Italy) – November.

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Presentation on theme: "Multiplicity analysis and dN/d  reconstruction with the silicon pixel detector Terzo Convegno Nazionale sulla Fisica di ALICE Frascati (Italy) – November."— Presentation transcript:

1 Multiplicity analysis and dN/d  reconstruction with the silicon pixel detector Terzo Convegno Nazionale sulla Fisica di ALICE Frascati (Italy) – November 12-14, 2007 Maria Nicassio (Univ. and INFN Bari) in collaboration with D. Elia, B. Ghidini (Univ. and INFN Bari) T. Virgili (Univ. Salerno)

2 Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12, Contents  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

3 Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12, Introduction  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

4 Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12, Introduction  Acceptance coverage: SPD layers: -2.0 <  < 2.0 (inner) -1.5 <  < 1.5 (outer)

5 Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12,  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 Introduction Fiducial window  

6 Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12, dN/d distributions (uncorrected) dN/d  distributions (uncorrected) asymmetry due to the detector efficiency losses in PDC06 Inner layer clusters Tracklets

7 Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12, Corrections: SPD acceptance (I)  MC data samples:  Pb-Pb (HijingParam) 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] cmnVtxzbinx = 40  dVtx-Z = 1 cm

8 Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12,  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 layer  if there are 2 clusters on adjacent modules: track is counted twice  this takes into account the overlapping regions (  2%)  compute acceptance and error in each bin ( fAcc,fErrAcc )  statistics in each bin:  detectable tracks:  10 4  resulting error on the acceptance:  Corrections: SPD acceptance (II)

9 Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12,  Results:  convoluted acceptance & efficiency: Tracklets Inner layer Outer layer Corrections: SPD acceptance (III)

10 Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12,  Correction applied: Corrections: acceptance & efficiency (I)

11 Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12, Corrections: acceptance & efficiency (II)  Correction applied:

12 Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12, Corrections: background from secondaries (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

13 Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12,  Clusters (inner layer): Corrections: background from secondaries (II)

14 Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12,  Clusters (inner layer): correction (to be multiplied by) Corrections: background from secondaries (III)

15 Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12,  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: background from secondaries (IV)

16 Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12,  Tracklets: correction (to be multiplied by) Corrections: background from secondaries (V)

17 Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12, Corrections: vertex reconstruction (I)  Generated dN ch /d  N.B. The correction is integrated, but it should be a function of multiplicity and vertex position

18 Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12,  Correction (to be multiplied by) Corrections: vertex reconstruction (III) The correction depends both on  and on multiplicity at low multiplicity To be checked as a function of Z-vtx

19 Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12,  All corrections applied: inner layer clusters Final dN/d  distributions

20 Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12,  All corrections applied: tracklets Final dN/d  distributions

21 Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12, Multiplicity distributions (uncorrected)

22 Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12, Multiplicity distributions  Background correction: 16% 7% Background fractions for each event

23 Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12, Multiplicity distributions  All corrections applied:

24 Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12, MB2 =(GFO.and.V0OR).and.notBG Effect of trigger selection: first look (I)  Trigger correction:

25 Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12, Effect of trigger selection: first look (II)  Generated dN/d  : MB2 =(GFO.and.V0OR).and.notBG All events No trigger No vertex

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

27 Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12, Backup slides

28 Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12,  Couples of clusters associated with the same track Tracklet algorithm efficiency in p-p

29 Maria Nicassio Convegno Nazionale sulla Fisica di ALICE Frascati - November 12, Using the default cuts the algorithm efficiency is 99% Tracklet algorithm efficiency in p-p  Couples of clusters associated with the same track


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