Alignment of the ALICE Silicon tracking detectors

1
Alignment of the ALICE Silicon tracking detectors
A.Dainese INFN Padova for the ALICE Collaboration
2
Layout

• The ALICE Inner Tracking System (ITS) ? see also
  V. Manzari
• ITS alignment strategy
• Cosmics for alignment
• Validation of survey measurements
• Results from software alignment algorithms
• Summary and outlook

3
Inner Tracking System (ITS)

• Silicon Pixel Detector (SPD)
• 10M channels
• 240 sensitive vol. (60 ladders)

• Silicon Drift Detector (SDD)
• 133k channels
• 260 sensitive vol. (36 ladders)

• Silicon Strip Detector (SSD)
• 2.6M channels
• 1698 sensitive vol. (72 ladders)

SSD
ITS total 2198 alignable sensitive volumes ?
13188 d.o.f.
SPD
SDD
4
ITS detector resolutions target alignment
precisions
detector local c.s. xlocrfglob, ylocrglob,
zloc zglob

• Residual expected misalignment left after
  applying the realignment procedure(s). Target
  0.7?resol. ? 20 degradation of the resolution

5
Impact of ITS misalignmenton ITSTPC tracking
resolutions

• Effect of misalignment on track impact parameter
  to the primary vertex (d0), pt, vertex
  resolutions studied

d0 resolution
pt resolution
no misal misal 10 mm misal 20 mm misal 30 mm
no misal misal 10 mm misal 20 mm misal 30 mm
Target residual misalignment

• effect of misalignment
• ? large worsening
• factor 2 at high pt
• plus 5 mm

effect of misalignment above 10 GeV/c
6
ITS alignment with tracksgeneral strategy

• Data sets cosmics first pp collisions (and
  beam gas)
• use cocktail of tracks from cosmics and pp to
  cover full detector surface and to maximize
  correlations among volumes
• Start with B off, then switch on B (pp) ?
  possibility to select high-momentum (no multiple
  scattering) tracks for alignment
• General strategy
• validation of survey measurements with cosmics
• start with layers easier to calibrate SPD and
  SSD
• good resol. in rf (12-20mm), worse in z
  (120-830mm)
• global ITS alignment relative to TPC (already
  internally aligned)
• finally, inclusion of SDD, which need longer
  calibration (interplay between alignment and
  calibration)
• Two independent track-based alignment methods
• global Millepede (default method)
• local iterative method based on residuals
  minimization

7
Triggering and tracking the cosmics

• Trigger SPD FastOR
• Coincidence between top outer SPD layer
• and bottom outer SPD layer
• rate 0.18 Hz
• ITS Stand-Alone tracker adapted for cosmics
• pseudo-vertex point of closest approach between
  two tracklets built in the top and bottom SPD
  half-barrels
• Search for two back-to-back tracks starting from
  this vertex

8
Cosmic data sample

• Statistics collected (after reco) 105 events
  with B0 in 2008

Layer 5 (SSD)
Layer 4 (SDD)
Layer 1 (SPD)
Layer 4 (SDD)
Layer 4 (SDD)
Layer 1 (SPD)
Layer 1 (SPD)
9
Validation of Strips (SSD) survey with cosmics

• SSD survey 2 local shifts (x,z) 1 rotation (q)
• Modules on ladders (critical small stat on
  single modules with cosmics)
• precision 5 mm
• Ladders on support cones (important starting
  point for alignment)
• precision 15 mm
• Validation with cosmics. Three methods
• Extra clusters from acceptance overlaps ?
  distance between two clusters from same track on
  contiguous (overlapping) modules on same ladder
• Fit one track on outer layer, one on inner layer
  ? distance and angles between the two tracks
• Fit track on one SSD layer (2 points) ? residuals
  on other SSD layer

10
Validation of SSD survey with cosmics (1)

• Extra clusters from acceptance overlaps ?
  distance between two clusters from same track on
  contiguous (overlapping) modules on same ladder

module on ladder misalignment

• ???xy)25 ?m (48)
• ??point)25/v218 ?m
• ??misal)lt5 ?m (27)

• ???xy)26 ?m (36)
• ??point)26/v218 ?m
• ??misal)lt5 ?m (15)

11
Validation of SSD survey with cosmics (2)

• Fit one track on top half barrel, one on bottom
  ? distance and angles between the two tracks

12
Validation of SSD survey with cosmics (3)

• Fit track on one SSD layer (2 points) ? residuals
  on other SSD layer

overall module misalignment

• ??point)25 ?m
• ??misal)10 ?m

13
ITS alignment with Millepede

• Determine alignment parameters of all modules
  in one go, by minimizing the global c2 of
  track-to-points residuals for a large set of
  tracks (cosmics pp)
• The alignment of the ITS follows this
  hierarchical sequence

• SPD SECTORS (10)
• optionally SPD STAVES (60)
• SPD HALF-STAVES (120)
• SPD LADDERS (sensitive modules 240)
• SPD BARREL W.R.T. SSD BARREL
• SSD LADDERS (72)

14
Checking the quality of realignment
y

• Select muons with DCA to (0,0) lt 1 cm
• Main variable track-to-track Dxy at y0
• Acceptance overlaps ? extra clusters
• Alignment monitoring tool

x
15
Millepede SPD realignmentDxy at y0

• Track-to-track matching (2 points per track in
  the pixels)

Sim, ideal geom
DATA B0
not realigned
realigned
ALICE Preliminary
43 mm
48 mm
track-to-track Dxy cm
Expected spread
? sspatial14 mm ? smisal9 mm
? sspatial11 mm (Sim)
16
Millepede realignmentSPD extra clusters
Sim, ideal geom
DATA B0
not realigned
realigned
ALICE Preliminary
15 mm
20 mm
Expected spread
? sspatial14 mm? smisal9 mm
? sspatial11 mm (Sim)
17
Cross checks with B-on runs

• Stand-alone tracking in SPD (two points) and look
  at extra clusters to track distance (unaffected
  by curv.)

B0.5T
B0
ALICE Preliminary
17?2 mm
ALICE Preliminary
18?1 mm
OK
B-0.5T
ALICE Preliminary
20?2 mm
18
Millepede SPD realignmentgetting right the
hierarchy
non-hiearchical
hiearchical
ALICE Preliminary
19
Millepede SPD-SSD realignmentDxy at y0

• PixelsStrips two tracks (top and bottom) with 4
  pts each

Sim, ideal geom
DATA B0
realigned
ALICE Preliminary
19 mm
30 mm
track-to-track transverse distance at y0 cm
20
Millepede SPD-SSD realignmentDxy at y0

• PixelsStrips two tracks (top and bottom) with 4
  pts each

Simulation ideal geometry target of alignment
DATA B0
realigned
promising! already close to target
ALICE Preliminary
30 mm
track-to-track transverse distance at y0 cm

• single track impact parameter resolution
  30/v221mm
• but pt unknown ? need data with B-on (2009)

21
Alignment monitoring

• Excluding selected points from fit
• Compute residuals

SSD
fit points residuals
SPD
22
Alignment monitoring

• rf residuals in inner pixel layer, with B-off and
  B-on

data B0 s26mm
data B0.5T s26mm
data B-0.5T s26mm
simul B0.5T s17mm
23
A second alignment methodIterative local
approach

• Alignment params from minimization of residuals
• Local works on a module-by-module basis
• Iterations are used to take into account
  correlations between the alignment params of
  different modules
• For the pixels, similar alignment quality as
  Millepede

Millepede s 49 mm
Iterative s 52 mm
track-to-track transverse distance at y0 cm
track-to-track transverse distance at y0 cm
24
Comparison of Millepede and Iterative

• Comparison of alignment parameters is promising
  for
• estimate of residual misalignment / systematics
• investigations of possible problems (outliers)

25
Silicon Drift Detectors calibration alignment

• The two intermediate layers
• In SDD, local x determined from drift time
• xloc (t t0) vdrift
• two calibration parameters t0 and vdrift
• Interplay between alignment and calibration
• t0 and vdrift (also obtained from injectors) as
  additional parameters in Millepede

xloc
Geometry only
Geometry calibration
26
Summary Outlook

• ALICE Inner Tracking System alignment strategy
  presented
• Hierachical track-based alignment using global
  fit (Millepede)
• Strips (SSD) survey validated with cosmics
• Most of pixels (SPD) realigned to ?10 mm
• need cross-checks with pp, especially on the
  sides
• With SPDSSD ? track impact parameter resolution
  20 mm, but difficult to conclude because
  momentum is unknown (B0)
• Next steps
• include SDD (calibration ongoing)
• ITS-TPC alignment
• cosmics 2009 (data taking in progress)
• data with B0.5T ? performance VS pt!
• finalize alignment and monitoring for
  proton-proton

27
EXTRA SLIDES
28
TPC-ITS relative alignment

• Kalman-filter aligner with 8 model parameters
  misalignment of TPC wrt ITS (6 pars), TPC
  calibration (time zero, drift velocity)
• Procedure starts with matching of TPC tracks to
  ITS stand-alone tracks
• Propagate tracks to reference cylinder at R80 cm
  ? residuals
• The residuals are fed to the Kalman aligner for
  fitting
• Benchmarked on simulation ? OK
• First result on cosmic data with B0 (ITS aligned
  with Millepede)

29
TPC-ITS relative alignment

• TPC vdrift correction from TPC-ITS alignment VS
  same from TempPressure sensors ? difference
  lt0.5
• Data re-reconstructed with TPC-ITS alignment ?
  new realignment ? results consistent with 0 ? OK

(B on)
S.Rosseger
30
First look at statistics in pp

• 10k (scaled to 100k) pp events at 0.9 and 14 TeV
  B0.5T
• Minimum number of points for module in each layer
• 6 points tracks with ptgt250 MeV

? First Physics sample(s) look OK
(statistics-wise) to perform First Alignment
31
First look at statistics in ppchecking the
alignment with extra clusters

• 10k (scaled to 100k) pp events at 0.9 and 14 TeV
  B0.5T
• Number of extra clusters per layer per det type
  (assuming the tracking finds all of them)

? First Physics sample(s) look OK
(statistics-wise) to layer-by-layer check of
First Alignment with extra cls
32
Millepede SPD realignmentstability in time
Stability test with nine 10k-tracks subsamples
Dxy at y0 plots with same alignment file
From trk Mean (mm) Sigma (mm) 0
-13 57 10000 -4 54 20000
6 52 30000 -3 56 40000 -2 53 50000
1 53 60000 5 52 70000
8 52 80000 -1 54
Dxy cm
33
Analysis of Millepede results
Dxy cm
Strongest improvements when aligning staves
w.r.t. sectors