Alignment of the ALICE Si tracking detectors

1. Alignment of the ALICE Si 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. Residual: expected misalignment left after applying the realignment procedure(s). Target ~0.7resol. ~20% degradation of the resolution yloc zloc xloc ITS detector resolutions & target alignment precisions detector local c.s.: xloc~rfglob, yloc~rglob, zloc = zglob

5. Impact of ITS misalignmenton ITS+TPC tracking resolutions 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 Effect of misalignment on track impact parameter to the primary vertex (d0), pt, vertex resolutions studied

6. ITS alignment with tracks:general 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 AND • 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 Layer 5 (SSD) Layer 4 (SDD) Layer 1 (SPD) Layer 4 (SDD) Layer 4 (SDD) Layer 1 (SPD) Layer 1 (SPD) Statistics collected (after reco): ≈105 events with B=0 in 2008

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) module on ladder misalignment • xy)=25 m (48) • point)=25/√2=18 m • misal)<5 m (27) • xy)=26 m (36) • point)=26/√2=18 m • misal)<5 m (15) Extra clusters from acceptance overlaps  distance between two clusters from same track on contiguous (overlapping) modules on same ladder

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) overall module misalignment • point)~25 m • misal)~10 m Fit track on one SSD layer (2 points)  residuals on other SSD layer

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: ITS Schematic layout SPD • 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 x Select muons with DCA to (0,0) < 1 cm Main variable: track-to-track Dxy at y=0 Acceptance overlaps  “extra” clusters Alignment monitoring tool

15. Millepede SPD realignment:Dxy at y=0 Sim, ideal geom: DATA B=0 not realigned realigned ALICE Preliminary 43 mm 48 mm track-to-track Dxy [cm] Expected spread  sspatial=14 mm  smisal~9 mm  sspatial=11 mm (Sim) Track-to-track matching (2 points per track in the pixels)

16. Millepede realignment:SPD “extra” clusters Sim, ideal geom: DATA B=0: not realigned realigned ALICE Preliminary 15 mm 20 mm Expected spread  sspatial=14 mm smisal~9 mm  sspatial=11 mm (Sim)

17. Cross checks with B-on runs B=+0.5T B=0 ALICE Preliminary 172 mm ALICE Preliminary 181 mm OK B=-0.5T ALICE Preliminary 202 mm Stand-alone tracking in SPD (two points) and look at “extra” clusters to track distance (unaffected by curv.)

18. Millepede SPD realignment:getting right the hierarchy non-hiearchical hiearchical ALICE Preliminary

19. Millepede SPD-SSD realignment:Dxy at y=0 Sim, ideal geom: DATA B=0 realigned ALICE Preliminary 19 mm 30 mm track-to-track transverse distance at y=0 [cm] Pixels+Strips: two tracks (top and bottom) with 4 pts each

20. Millepede SPD-SSD realignment:Dxy at y=0 Simulation: ideal geometry target of alignment DATA B=0 realigned promising! already close to target ALICE Preliminary 30 mm track-to-track transverse distance at y=0 [cm] • single track impact parameter resolution 30/√2~21mm • but pt “unknown”  need data with B-on (2009) Pixels+Strips: two tracks (top and bottom) with 4 pts each

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 B=0 s=26mm data B=+0.5T s=26mm data B=-0.5T s=26mm simul B=+0.5T s=17mm

23. A second alignment method:Iterative local approach Millepede s = 49 mm Iterative s = 52 mm track-to-track transverse distance at y=0 [cm] track-to-track transverse distance at y=0 [cm] 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:

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 xloc Geometry only Geometry + calibration • 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

26. Cosmics 2009 Show validation of 2008 alignment

27. 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 SPD+SSD  track impact parameter resolution ~20 mm, but difficult to conclude because momentum is unknown (B=0) • Next steps: • include SDD (calibration ongoing) • ITS-TPC alignment • cosmics 2009 (data taking in progress) • data with B=0.5T  performance VS pt! • finalize alignment and monitoring for proton-proton

28. EXTRA SLIDES

29. 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 R=80 cm  residuals The residuals are fed to the Kalman aligner for fitting Benchmarked on simulation  OK First result on cosmic data with B=0 (ITS aligned with Millepede)

30. TPC-ITS relative alignment (B on) S.Rosseger TPC vdrift correction from TPC-ITS alignment VS same from Temp&Pressure sensors  difference <0.5% Data re-reconstructed with TPC-ITS alignment  new realignment  results consistent with 0  OK

31. First look at statistics in pp  First Physics sample(s) look OK (statistics-wise) to perform First Alignment • 10k (scaled to 100k) pp events at 0.9 and 14 TeV B=0.5T • Minimum number of points for module in each layer • 6 points tracks with pt>250 MeV ALICE Physics Forum, CERN, 25.03.2009 Andrea Dainese

32. First look at statistics in pp:checking the alignment with extra clusters  First Physics sample(s) look OK (statistics-wise) to layer-by-layer check of First Alignment with extra cls 10k (scaled to 100k) pp events at 0.9 and 14 TeV B=0.5T Number of extra clusters per layer per det type (assuming the tracking finds all of them) ALICE Physics Forum, CERN, 25.03.2009 Andrea Dainese

33. Millepede SPD realignment:stability in time Stability test with nine 10k-tracks subsamples Dxy at y=0 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]

34. Analysis of Millepede results Dxy [cm] Strongest improvements when aligning staves w.r.t. sectors