Terzo Convegno sulla Fisica di ALICE - LNF, Andrea Dainese 1 Preparation for ITS alignment A. Dainese (INFN – LNL) for the ITS alignment group (CERN, LNL, NIKHEF, PD, TS) What? ITS detectors, target alignment precision Why? Impact of misalignments Why? Impact of misalignments How? Strategy and methods How? Strategy and methods How well? First results from simulation How well? First results from simulation

Terzo Convegno sulla Fisica di ALICE - LNF, Andrea Dainese 2 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) SPD SSD SDD ITS total: 2198 alignable sensitive volumes  d.o.f.

Terzo Convegno sulla Fisica di ALICE - LNF, Andrea Dainese 3 ITS detector resolutions & target alignment precisions Full: initial misalignments as expected from the mechanical imprecision after installation, actually set to  m at the sensor level, probably higher at the ladder or layer level (~100  m), more later... Residual: expected misalignment left after applying the realignment procedure(s). Target ~0.7  resol.  ~20% degradation of the resolution SPD (r = 4 & 7 cm) SDD (r = 14 & 24 cm) SSD (r = 39 & 44 cm) nom. resolutions x loc  (y loc )  z loc [  m 3 ] 12  (0)   (0)  2020  (0)  830 full mis. (shifts) x loc  y loc  z loc [  m 3 ] 20  20  2045  45  4530  30  100 residual mis. (shifts) x loc  y loc  z loc [  m 3 ] 10  10  2020  20  2015  15  100 rotations (mrad) around x loc,y loc,z loc 0.3 y loc z loc x loc detector local c.s.: x loc ~r  glob, y loc ~r glob, z loc = z glob

Terzo Convegno sulla Fisica di ALICE - LNF, Andrea Dainese 4 Primary Vertex B e X d0d0 rec. track Impact of ITS misalignment on tracking performance Effect of misalignment on d 0 (and p t ) resolutions studied by reconstructing misaligned events with ideal geometry Estimated effect on D 0  K  significance A.D, A.Rossi null residual full full+

Terzo Convegno sulla Fisica di ALICE - LNF, Andrea Dainese 5 Introduction of “realistic” misalignment Why “realistic”? 1.misalignment should follow hierarchy of hardware structure; each level should be misaligned (and then realigned) 2.magnitude of initial misalignments should be realistic (input from hardware experts) 3.misalignments at the same hierarchical level should be correlated E.g., for SPD: barrel / half-barrel / sector / half-stave / ladder # sensitive volumes: 240 / 120 / 24 / 2 / 1 magnitude of misal. (  m): <1000 / ~100 / ~100 / / 5 (up to now only the ladder was misaligned) Transition to realistic misalignment is in progress (requires changes to the ITS geometry) Will provide: better playground for preparation of realignment procedures better estimate of effects of residual misalignments on performance

Terzo Convegno sulla Fisica di ALICE - LNF, Andrea Dainese 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  possibility to select high-momentum (no multiple scattering) tracks for alignment General strategy: 1)start with layers easier to calibrate: SPD and SSD  good resol. in r  (12-20  m), worse in z (  m)  ITS z resol. provided by SDD anode coord. (20  m)  easily calibrated  can be included from the beginning in alignment chain 2)global ITS alignment relative to TPC (already internally aligned) 3)finally, inclusion of SDD (drift coord: r  ), which probably need longer calibration (interplay between alignment and calibration) Two independent track-based alignment methods in preparation: global: Millepede 1 (ported to ALICE for muon arm alignment) local: iterative method based on residuals minimization

Terzo Convegno sulla Fisica di ALICE - LNF, Andrea Dainese 7 Preparation for cosmics data (1)  Cosmics Run in February 2008  Expected muon rate through ITS inner layer (~200cm 2, ~40m underground): ~0.02 Hz (  ~10 4  /week) Trigger with SPD layers (tracks with points) : Trigger with ACORDE (“peripheral” tracks with 4-8 points in SSD-SDD) FastOR (FO) of the 20 chips on 2 half-staves For each half-barrel (A side, C side):  20 FOs outer layer, 10 FOs inner layer  Any logic combination of these 30 FOs SPD FastOR Option being considered for cosmics: 2Layers coinc. (≥2FOs inn layer & ≥2FOs out layer) purity (fraction with 1  with 4 SPD hits) : ~97%, inefficiency (fraction of lost  with 4 SPD hits) : ~19% A side C side D.Elia

Terzo Convegno sulla Fisica di ALICE - LNF, Andrea Dainese 8 Preparation for cosmics data (2) Cosmics at ALICE: p > 10 GeV/c, ~20 GeV/c (Hebbeker, Timmermans, 2001) Cosmics tracking in ITS: modified stand-alone ITS tracking (cluster-grouping algorithm from Torino)  98% efficiency (12 points, 6inward+6outward) for muons that leave 12 hits, with B=0 and B=0.5T  high eff (~80-90%) also for muons crossing only outer layers robustness tested with “extreme” misalignment scenarios tracks prolongation from TPC to ITS being optimized for cosmics Preparing first d 0 resolution meas. by cosmics two-track matching  d0 =12  m  d0 =21  m no misal. full misal. A.D., A.Jacholkowski

Terzo Convegno sulla Fisica di ALICE - LNF, Andrea Dainese 9 Cosmics in the ITS 50k  ’s through inner ITS layer (~5 weeks of cosmics data) Track multiplicity per module: SPD inner A.Rossi 6 pts tracks 12 pts tracks Volume correlations. Number of modules correlated to a given module: SPD innerSDD innerSSD inner

Terzo Convegno sulla Fisica di ALICE - LNF, Andrea Dainese 10 Track-based alignment in the barrel (ITS, TPC, TRD,...) - Framework Overview - ESD file with track space points ESD file with track space points ESD file with track space points Tree with selected space points Alignment procedures Local file Local Iterative residuals minimization Millepede... Distributed/ CAF Reconstruction C.Cheskov

Terzo Convegno sulla Fisica di ALICE - LNF, Andrea Dainese 11 ITS alignment with tracks: the global approach of Millepede M.Lunardon, S.Moretto Determine alignment parameters of “all” modules in one go, by minimizing the global  2 of track-to-points residuals for a large set of tracks (cosmics + pp) Linear problem: the points residuals can be expressed as a linear function of the (global, d i ) alignment params and (local δ i ) tracks’ params At the moment being tested with cosmics tracks and B=0 Strategy for tests: 1.validate algorithm with “fast simulation” (no detector response) 2.introduce full detector response

Terzo Convegno sulla Fisica di ALICE - LNF, Andrea Dainese 12 x LOC y LOC z LOC 5 weeks of cosmics (50k in SPD1), B=0 Tracks with 12 pts 375 modules (out of 2198) SPDSDD SSD input result difference Millepede with fast simulation y loc z loc x loc

Terzo Convegno sulla Fisica di ALICE - LNF, Andrea Dainese 13 As a function of the number of minimum # of points per track SPD x y z    Millepede with fast simulation RMS of (result-input) 5 weeks of cosmics (50k in SPD1), B=0 Also tracks with <12 pts SDD similar to SPD better with “peripheral tracks” 5  m!

Terzo Convegno sulla Fisica di ALICE - LNF, Andrea Dainese 14 As a function of the number of minimum # of points per track SSD x y z    Millepede with fast simulation RMS of (result-input) 5 weeks of cosmics (50k in SPD1), B=0 Also tracks with <12 pts    much better with “peripheral tracks”

Terzo Convegno sulla Fisica di ALICE - LNF, Andrea Dainese 15 Millepede: fast vs. full simulation Deterioration of results with full detector simulation This triggered investigations on the different steps of the simulation, with misalignment  found problems with overlaps that caused shift of ITS rec. points  will be solved in new ITS geometry For the moment, continue alignment studies without misalignments

Terzo Convegno sulla Fisica di ALICE - LNF, Andrea Dainese 16 PARAM SPDSDDSSD meanRMSmeanRMSmeanRMS x (  m) y (  m) z (  m)  (mdeg)  (mdeg)  (mdeg) about 5 weeks of cosmics, B=0 663 modules ( 135 SPD SDD SSD ) Millepede: full simul., no misal. M.Lunardon, S.Moretto

Terzo Convegno sulla Fisica di ALICE - LNF, Andrea Dainese 17 Millepede: full simul., no misal. SPD only, SPD+SSD about 5 weeks of cosmics, B=0 612 modules ( 192 SPD SSD ) PARAM SPD RMSSSD RMS SPD onlySPD+SSD x (  m) 8614 y (  m) z (  m)  (mdeg)  (mdeg)  (mdeg) SPD: worse on the sides  need pp tracks z  top bottom M.Lunardon, S.Moretto

Terzo Convegno sulla Fisica di ALICE - LNF, Andrea Dainese 18 ITS alignment with tracks: iterative local method Iterations loop (user-defined) Loop over volumes (user-defined) Update alignment objects Align volume(s) A w.r.t. volume(s) B OCDB Fit points in volume(s) B (several fitters available for straight line and helix tracks) and extrapolate tracks to volume(s) A Minimize (several minimizers available), w.r.t. alignment params of volume(s) A, residuals between points and extrapolations in volume(s) A C.Cheskov A.Rossi Determines alignment params by minimizing track-to-points residuals Local: works on a module-by-module basis Iterations are used to take into account correlations between the alignment params of different modules

Terzo Convegno sulla Fisica di ALICE - LNF, Andrea Dainese 19 PARAM SPD RMSSDD RMSSSD RMS IterMillepIterMillepIterMillep x (  m) y (  m) z (  m)  (mdeg)  (mdeg)  (mdeg) about 5 weeks of cosmics, B=0 no iterations (not necessary without misalignment) Iterative local method: full simul., no misal. A.Rossi

Terzo Convegno sulla Fisica di ALICE - LNF, Andrea Dainese 20 Iterative local method: full simul. with misal., iterations SPD inner: mean, RMS global  global z (cm)  x (  m) A.Rossi iterations  convergence worse on the sides  need pp tracks

Terzo Convegno sulla Fisica di ALICE - LNF, Andrea Dainese 21 ITS-TPC relative alignment Relative alignment of ITS and TPC (3 shifts + 3 angles) with straight tracks (including cosmics) Alignment requirements: given by TPC resolutions: shifts: ~100  m angles: ~0.1 mrad Method (under development): Assume that TPC and ITS are already internally aligned and calibrated Use independently fitted tracks in the ITS and the TPC Alignment params are estimated by a Kalman filter algorithm Proof-of-principle test with “toy” tracks M.Krzewicki

Terzo Convegno sulla Fisica di ALICE - LNF, Andrea Dainese 22 Summary The ITS alignment challenge: determine 13,000 parameters with a precision of ~10  m Track-based alignment using cosmics and pp collisions preparation for cosmics reconstruction in ITS Two independent algorithms under development Millepede (global) local iterative method Promising results even with cosmics only, should be much better with cosmics + pp tracks ITS alignment relative to TPC also under study

Terzo Convegno sulla Fisica di ALICE - LNF, Andrea Dainese 23 EXTRA SLIDES

Terzo Convegno sulla Fisica di ALICE - LNF, Andrea Dainese 24 1.A muon direction is generated 2.Intersection points with misaligned detectors are evaluated in local coordinate systems large misal. order of 100  m 3.Points smeared with given resolutions 4.Use tracks with 4-12 points Advantages w.r.t. standard sim: clean situation, without simulation/reconstruction effects faster Fast Simulation