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Preparation for ITS alignment. What? ITS detectors, target alignment precision Why? Impact of misalignments How? Strategy and methods How well? First results from simulation. A. Dainese (INFN – LNL) for the ITS alignment group (CERN, LNL, NIKHEF, PD, TS). Inner Tracking System (ITS).

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Preparation for its alignment

Preparation for ITS alignment

  • What? ITS detectors, target alignment precision

  • Why? Impact of misalignments

  • How? Strategy and methods

  • How well? First results from simulation

A. Dainese

(INFN – LNL)

for the ITS alignment group (CERN, LNL, NIKHEF, PD, TS)

Terzo Convegno sulla Fisica di ALICE - LNF, 14.11.07 Andrea Dainese


Inner tracking system its
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

Terzo Convegno sulla Fisica di ALICE - LNF, 14.11.07 Andrea Dainese


Its detector resolutions target alignment precisions

yloc

zloc

xloc

ITS detector resolutions & target alignment precisions

  • Full: initial misalignments as expected from the mechanical imprecision after installation, actually set to 20-45 mm at the sensor level, probably higher at the ladder or layer level (~100 mm), more later...

  • Residual: expected misalignment left after applying the realignment procedure(s). Target ~0.7resol. ~20% degradation of the resolution

detector local c.s.: xloc~rfglob, yloc~rglob, zloc = zglob

Terzo Convegno sulla Fisica di ALICE - LNF, 14.11.07 Andrea Dainese


Impact of its misalignment on tracking performance

rec. track

e

Primary

Vertex

B

X

d0

Impact of ITS misalignmenton tracking performance

  • Effect of misalignment on d0 (and pt) resolutions studied by reconstructing misaligned events with ideal geometry

  • Estimated effect on D0Kp significance

null

residual

full

full+

A.D, A.Rossi

Terzo Convegno sulla Fisica di ALICE - LNF, 14.11.07 Andrea Dainese


Introduction of realistic misalignment
Introduction of “realistic” misalignment

  • Why “realistic”?

    • misalignment should follow hierarchy of hardware structure; each level should be misaligned (and then realigned)

    • magnitude of initial misalignments should be realistic (input from hardware experts)

    • 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. (mm):<1000 / ~100 / ~100 / 10-50 / 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, 14.11.07 Andrea Dainese


Its alignment with tracks general strategy
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:

    • start with layers easier to calibrate: SPD and SSD

      • good resol. in rf (12-20mm), worse in z (120-830mm)

      • ITS z resol. provided by SDD anode coord. (20mm)  easily calibrated  can be included from the beginning in alignment chain

    • global ITS alignment relative to TPC (already internally aligned)

    • finally, inclusion of SDD (drift coord: rf), 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, 14.11.07 Andrea Dainese


Preparation for cosmics data 1 cosmics run in february 2008

Expected muon rate through ITS inner layer (~200cm2, ~40m underground): ~0.02 Hz ( ~104m/week)

Trigger with SPD layers (tracks with 10-12 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

Preparation for cosmics data (1) Cosmics Run in February 2008 

D.Elia

A side

SPD

FastOR

C side

  • Option being considered for cosmics:

    2Layers coinc. (≥2FOs inn layer & ≥2FOs out layer)

    purity (fraction with 1 m with 4 SPD hits): ~97%,

    inefficiency (fraction of lost m with 4 SPD hits): ~19%

Terzo Convegno sulla Fisica di ALICE - LNF, 14.11.07 Andrea Dainese


Preparation for cosmics data 2

no

misal.

full

misal.

sd0=12mm

sd0=21mm

Preparation for cosmics data (2)

  • Cosmics at ALICE: p > 10 GeV/c, <p>~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 d0 resolution meas. by cosmics two-track matching

A.D.,

A.Jacholkowski

Terzo Convegno sulla Fisica di ALICE - LNF, 14.11.07 Andrea Dainese


Cosmics in the its

SPD inner

SDD inner

SSD inner

103

103

103

102

102

102

10

10

10

1

1

1

800

800

400

400

Cosmics in the ITS

A.Rossi

  • 50k m’s through inner ITS layer (~5 weeks of cosmics data)

  • Track multiplicity per module:

  • Volume correlations. Number of modules correlated to a given module:

SPD inner

6 pts

tracks

12 pts

tracks

Terzo Convegno sulla Fisica di ALICE - LNF, 14.11.07 Andrea Dainese


Track based alignment in the barrel its tpc trd framework overview
Track-based alignment in the barrel (ITS, TPC, TRD, ...)- Framework Overview -

C.Cheskov

Reconstruction

Reconstruction

Reconstruction

Reconstruction

Terzo Convegno sulla Fisica di ALICE - LNF, 14.11.07 Andrea Dainese


Its alignment with tracks the global approach of millepede
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 c2 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, di) alignment params and (local δi)tracks’ params

  • At the moment being tested with cosmics tracks and B=0

  • Strategy for tests:

    • validate algorithm with “fast simulation” (no detector response)

    • introduce full detector response

Terzo Convegno sulla Fisica di ALICE - LNF, 14.11.07 Andrea Dainese


Millepede with fast simulation

yloc

zloc

xloc

Millepede with fast simulation

SSD

SPD

SDD

  • 5 weeks of cosmics

  • (50k in SPD1), B=0

  • Tracks with 12 pts

  • 375 modules

  • (out of 2198)

xLOC

input

result

difference

yLOC

zLOC

Terzo Convegno sulla Fisica di ALICE - LNF, 14.11.07 Andrea Dainese


Millepede with fast simulation rms of result input
Millepede with fast simulationRMS of (result-input)

As a function of the number of minimum # of points per track

  • 5 weeks of cosmics

  • (50k in SPD1), B=0

  • Also tracks

  • with <12 pts

SPD

x yz

5mm!

better with “peripheral tracks”



SDD similar

to SPD

Terzo Convegno sulla Fisica di ALICE - LNF, 14.11.07 Andrea Dainese


Millepede with fast simulation rms of result input1
Millepede with fast simulationRMS of (result-input)

As a function of the number of minimum # of points per track

  • 5 weeks of cosmics

  • (50k in SPD1), B=0

  • Also tracks

  • with <12 pts

SSD

x yz

much better with “peripheral tracks”





Terzo Convegno sulla Fisica di ALICE - LNF, 14.11.07 Andrea Dainese


Millepede fast vs full simulation
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, 14.11.07 Andrea Dainese


Millepede full simul no misal
Millepede: full simul., no misal.

  • about 5 weeks of cosmics, B=0

  • 663 modules ( 135 SPD + 104 SDD + 424 SSD )

M.Lunardon, S.Moretto

Terzo Convegno sulla Fisica di ALICE - LNF, 14.11.07 Andrea Dainese


Millepede full simul no misal spd only spd ssd

z

f

bottom

top

SPD: worse on the sides

 need pp tracks

Millepede: full simul., no misal.SPD only, SPD+SSD

  • about 5 weeks of cosmics, B=0

  • 612 modules ( 192 SPD + 420 SSD )

M.Lunardon, S.Moretto

Terzo Convegno sulla Fisica di ALICE - LNF, 14.11.07 Andrea Dainese


Its alignment with tracks iterative local method
ITS alignment with tracks: iterative local method

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, 14.11.07 Andrea Dainese


Iterative local method full simul no misal
Iterative local method: full simul., no misal.

  • about 5 weeks of cosmics, B=0

  • no iterations (not necessary without misalignment)

A.Rossi

Terzo Convegno sulla Fisica di ALICE - LNF, 14.11.07 Andrea Dainese


Iterative local method full simul with misal iterations
Iterative local method:full simul. with misal., iterations

SPD inner: mean, RMS

iterationsconvergence

Dx (mm)

global z (cm)

global f

worse on the sides

 need pp tracks

A.Rossi

Terzo Convegno sulla Fisica di ALICE - LNF, 14.11.07 Andrea Dainese


Its tpc relative alignment
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 mm

    • 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, 14.11.07 Andrea Dainese


Summary
Summary

  • The ITS alignment challenge: determine 13,000 parameters with a precision of ~10 mm

  • 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, 14.11.07 Andrea Dainese


Extra slides
EXTRA SLIDES

Terzo Convegno sulla Fisica di ALICE - LNF, 14.11.07 Andrea Dainese


Fast simulation
Fast Simulation

  • A muon direction is generated

  • Intersection points with misaligned detectors are evaluated in local coordinate systems

    • large misal. order of 100 mm

  • Points smeared with given resolutions

  • Use tracks with 4-12 points

  • Advantages w.r.t. standard sim:

  • clean situation, without simulation/reconstruction effects

  • faster

Terzo Convegno sulla Fisica di ALICE - LNF, 14.11.07 Andrea Dainese


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