ATLAS Alignment Strategy

26. September 2007: VERTEX07 – Tobias Golling – ATLAS Alignment Strategy Page 13

Strategy

MC Simulation Challenge

Data Challenge

The ATLAS Experiment

22m

45m

26. September 2007: VERTEX07 – Tobias Golling – ATLAS Alignment Strategy Page 13

The Inner Detector

Pixels:

5 cm from beam

50 x 400 µm

Resolution:

12 x 100 µm

SCT (microstrips):

80 µm pitch

40 mrad stereo angle

Resolution:

16 x 580 µm

TRT (straw tracker):

96 barrel modules

28 end-cap disks

Resolution: 170 µm

2.2m

5.5m

26. September 2007: VERTEX07 – Tobias Golling – ATLAS Alignment Strategy Page 13

The Challenge

Tracking detectors unprecedented in size and complexity

⇒ 35 000 degrees of freedom

Exploit excellent intrinsic resolution of the precision tracking devices

Degradation of tracking resolution < 20%

Understanding of the momentum scale to 0.1%

⇒ Need for high accuracy alignment

⇒ Precision in r < 10 µm

Align muon spectrometer to 30 µm

over a distance of many meters

26. September 2007: VERTEX07 – Tobias Golling – ATLAS Alignment Strategy Page 13

Line of Attack

Detector design:

Material, resolution, redundancy, stability

Optimize overlap, layer coverage,...

Construction:

Measure & survey at all stages of assembly if possible

Operation:

Dedicated data processing & storage for alignment

The actual alignment: Track-based at the core, hardware alignment, represent

true DOFs, manpower vs software diversity, provide early (prelim.) alignment

Control systematic biases & “weak modes”: Combine complementary/external

constraints, relate modules across detector using different track topologies

Validation: Realistic deformation scenarios in simulation, exercise on real data

(cosmics), alignment monitoring, be prepared for the unexpected!

26. September 2007: VERTEX07 – Tobias Golling – ATLAS Alignment Strategy Page 13

Alignment Infrastructure

Alignment database structure

reflects underlying hardware:

Level 1 – subdetectors (few DOF's)

Level 2 – layers (~100 DOFs)

Level 3 – modules (35k DOFs)

Level 4 – module non-planarities

Raw

Data

Express stream

Xpress

Reco

Feedback in ~24h

Calibration

Alignment

Validation: DQ monitoring

Alignment monitoring

Rapid alert

Bulk reconstruction

O(10%) of all events,

include cosmics

26. September 2007: VERTEX07 – Tobias Golling – ATLAS Alignment Strategy Page 13

Alignment Monitoring

Assess alignment quality

Assess need for re-alignment

Study systematic deformations

26. September 2007: VERTEX07 – Tobias Golling – ATLAS Alignment Strategy Page 13

Monitor:

Hit efficiency

Generic track distributions

Residuals

Resonances, long lived particles

Electrons

Muons in muon spectrometer

Cosmics

Reconstructed beamline

...

Automatically evaluate agreement

with reference (mean, width, KS,...)

monitored

reference

Start from the Survey – 3 Examples

1) Survey of Pixel modules on sector (very stable!):

Measurement precision (optical) ~ 1 micron

Positioning precision < 3 microns

⇒ Constrain nearby modules on rigid body in alignment

2) Photogrammetry of SCT barrels:

Ellipse shape (3-point mount scenario)

Twist

3) Pixel module out-of-plane distortions:

Correct for bow in reconstruction

26. September 2007: VERTEX07 – Tobias Golling – ATLAS Alignment Strategy Page 13

Hardware-based Alignment

Goal for 1 TeV muon:

sagitta of 500 µm measured

with 50 µm accuracy

Sense wire placement:

20 µm precision

Single muon chamber

spatial resolution: 40 µm

⇒ alignment accuracy: 30 µm

Optical alignment system fully operation @ design

resolution (first results for endcap): RASNIKs & BCAMs

Complemented by track-based alignment:

Overlaping chambers

Light source

(coded mask)

Lens

CCD

26. September 2007: VERTEX07 – Tobias Golling – ATLAS Alignment Strategy Page 13

Short term thermally induced distortions?

⇒ Frequency Scanning Interferometry

Geodetic grid of length measurements

☹ No absolute position measurement

☺ Quasi real-time tracking of shape changes

☺ Precision < 1 micron

Muon Spectrometer

SCT

Track-based Alignment

Deduce alignment corrections from track fit quality

Necessary criterion:

Minimize residuals (distance from track to hit)

(2 minimization)

Vector of hit residuals r(a,π)

a: alignment parameters

π: track parameters

Covariance matrix V

Level 1

Level 2

Level 3

2 alignment scenarios:

Level 2

Level 1

Level 3

Per sub-detector

26. September 2007: VERTEX07 – Tobias Golling – ATLAS Alignment Strategy Page 13

3 Different Approaches

The Conservative: Local 2 Alignment Algorithm

Simplify r(a,π) = r(a), V replaced by its diagonal

⇒ System of equations breaks down to 6 × 6 blocks

Iterative to make up for neglected correlations

The Robust Alignment Algorithm

Add module overlaps in r and z

Less affected by misalignment in other layers

Less sensitive to multiple Coulomb scattering

Ring closure as additional constraint

Small size of overlap

The Cutting-Edge: Global 2 Alignment Algorithm

Accounting for all correlations

Requires inversion of a symmetric 35k × 35k matrix

Numerical challenge: Memory, precision, CPU time

Digits

Track reconstruction

Alignment

algorithm

Modify

geometry

& refit

Final alignment constants

⇒ All 3 algorithms give

comparable results

26. September 2007: VERTEX07 – Tobias Golling – ATLAS Alignment Strategy Page 13

“Weak Modes”

⇒ Residuals alone not good enough!

- Physics quantities, e.g. mass, vertex constraint

- Different event topologies: Off-axis cosmics, beam halo

- Redundancy (TRT, muon spectrometer, EM calorimeter)

- Survey

Minimizing residuals is necessary but not sufficient

Weak modes: systematic deformatins, track 2 ~ unchanged, track parameters biased

⇔ tracks are fitted to WRONG helices

⇒ No sensitivity in out-of-plane direction

track

module

hit

Example 1:

Example 2:

Barrels rotated about symmetry axis ~ radius

⇒ pT bias (charge dependent)

Example 3: “Telescope mode”

Barrels translated along beam ~ radius

⇒ , z0 bias

26. September 2007: VERTEX07 – Tobias Golling – ATLAS Alignment Strategy Page 13

The MC Simulation Challenge

Test alignment strategy before data is available

Simulate a typical as-built detector geometry in GEANT

inspired by survey data, assembly & installation precisions

also simulate typical weak modes

⇒ Large scale data simulation (time consuming)

In addition:

Dedicated study of characteristic and realistic weak modes

by implementing misalignment in the reconstruction (time efficient)

⇒ Look at one example of additional information beyond residuals

Identify weak modes with Global 2: Weak modes correspond to smallest eigenvalues,

eigenvectors = corresponding deformations

26. September 2007: VERTEX07 – Tobias Golling – ATLAS Alignment Strategy Page 13

Validation with EM Calorimeter

EM [rad]

∆track,EM [rad]

EM spatial resolution @  = 0:

 (E=50GeV) = 0.85 mrad

 (E=20GeV) = 1.3 mrad

Zee events:

⇨does not require absolute

calorimeter calibration

ℒ=250 nb-1

(~1 store @ 1031)

perfect

misalign1

misalign2

Charge asym. =

Use mainly W's and Z's

26. September 2007: VERTEX07 – Tobias Golling – ATLAS Alignment Strategy Page 13

Combined Test Beam Data (CTB)

CTB 2004:

First ever “ATLAS data” from subsystems combined

Large statistics (~20M tracks) at different beam momenta (2-180 GeV)

e+, e- and , with and without magnetic field

Challenge for alignment: small setup, collimated beam through narrow tower of modules

⇒ Certain DOFs unconstrained

z

y

x

8 SCT

modules

6 Pixel

modules

Followed by 6 TRT modules

Before

After

26. September 2007: VERTEX07 – Tobias Golling – ATLAS Alignment Strategy Page 13

Cosmic Rays & Beam Halo

Complementary (in angle)

Cosmics good for barrel,

beam halo good for end-caps

Cosmics:

High momentum (> 2 GeV), optionally no B-field

constrains pT-biasing modes

Relate top to bottom hemisphere ⇒ constrains telescope mode

Off-axis ⇒ constrains twist, elliptical distortions

Low rate (MC simulation: 0.5 Hz through Pixel barrel), non-uniform illumination

PT [GeV]

26. September 2007: VERTEX07 – Tobias Golling – ATLAS Alignment Strategy Page 13

SCT/TRT Cosmics – Data on Surface

2006 combined SCT & TRT barrel cosmic run:

22% SCT, 13% TRT barrel

No B-field

~400k events

Scintillator trigger

Require coincidence

SCT & TRT endcap cosmic run:

Validation of survey data:

Usage of SCT survey information

improves residuals by 10-20%

TRT barrel, Local 2: the algorithm converges

iteration

26. September 2007: VERTEX07 – Tobias Golling – ATLAS Alignment Strategy Page 13

Pixel Cosmics – Data on Surface

End of 2006:

Whole Pixel EC (10%)

Turned by 90o

Maximize acceptance

No B-field

Scintillator trigger

Require coincidence

Overlap alignment vs.

survey data

26. September 2007: VERTEX07 – Tobias Golling – ATLAS Alignment Strategy Page 13

Validation of survey

& alignment!

Residuals before and after alignment:

06/25/2007:

The Pixel detector goes

100 meters down into the pit

06/26/2007:

Insertion into the ID

06/28/2007:

Pixel detector in final position

ID installation complete

Beginning of 2008:

Complete ID operational

First alignment before collision data with cosmics

Cosmics now – Data in the Pit

ATLAS Milestone No. 4:

Cosmics data taking

Commissioning

Including:

Muon system (+ L1 RPC trigger)

TileCal (+ L1 barrel trigger)

TRT

⇒ 20 Hz to tape

>20k TRT tracks reconstructed:

TRT calibration & alignment

ongoing

Plan for independent trigger:

Scintillators (size of TRT barrel)

on top of ATLAS!

TRT configuration

26. September 2007: VERTEX07 – Tobias Golling – ATLAS Alignment Strategy Page 13

08/23/2007 – 09/02/2007

Conclusion & Outlook

We are aware of the challenge!

⇒ Complex but doable alignment strategy:

Data challenge:

Alignment algorithms exercised on real data

Validation of alignment results with survey data

MC simulation challenge:

Validate alignment strategy with MC simulation

Weak modes are hard to control

⇒ Bootstrap / additional information

First look at cosmics:

Helps to constrain weak modes

First alignment with cosmics beginning of 2008

Beware the

Münchhausen catch!

26. September 2007: VERTEX07 – Tobias Golling – ATLAS Alignment Strategy Page 13

Backup

26. September 2007: VERTEX07 – Tobias Golling – ATLAS Alignment Strategy Page 13

The Inner Detector

26. September 2007: VERTEX07 – Tobias Golling – ATLAS Alignment Strategy Page 13

Beam Halo

µ

µ

ATLAS

26. September 2007: VERTEX07 – Tobias Golling – ATLAS Alignment Strategy Page 13

Survey Constraints

Reference modules

Reference modules

Test

Test

Test

Test

Reference modules

(Current alignment)

(Survey alignment)

Step 1

Step 2

26. September 2007: VERTEX07 – Tobias Golling – ATLAS Alignment Strategy Page 13

Survey data describes test module's position wrt reference modules

Survey contains no information on modules' absolute positions

Step 1:

Find global transformation of reference modules from survey alignment to current alignment

Exclude the “test module” ⇒ this is equivalent to an unbiased residual where you exclude the hit on the “test layer”

Step 2:

The alignment correction is now given by:

the transformation of the test module from the current position to the predicted position from the survey

26. September 2007: VERTEX07 – Tobias Golling – ATLAS Alignment Strategy Page 13

Solving Large Systems of Equations

Sparse matrix techniques:

No real matrix inversion

Just the solution

No additional information (eigenvalues, eigenvectors, errors, etc.)

Fast ⇒ less than 10 min for 35k in a single CPU

Performance for the Pixel system (12.5k DOFs):

All have similar and adequate accuracy

26. September 2007: VERTEX07 – Tobias Golling – ATLAS Alignment Strategy Page 13

FSI – Frequency Scanning Interferometry

Question: Short term thermally induced distortions?

Temperature stability in the pit?

Mechanical coupling of sensors to cooling pipes & power cables

Answer: FSI

Geodetic grid of length measurements between nodes attached to SCT support structure

Measure 842 grid lines simulataneously

Repeat every 10 minutes to measure time variation

Precision < 1 micron

No absolute position measurement!

Benefits:

Quasi real-time tracking of shape changes

Define periods of detector stability for alignment

Need to model correlation between:

FSI measurement – module position

Can We Even Improve?

Of course, one always can!

Data model support for alignment:

Event, trigger selection

Minimize statistical correlation between modules by prescaling hits on tracks

Provide estimate of uncertainty for each alignment set

Longer timescale:

Estimate material description, thermal effects, B-field, vibrations,

location of charge deposition in silicon,...

Periodicity of alignment procedure – FSI

Use even more complementary constraints:

control systematics, cover all DOFs

Be really prepared for the unexpected!

26. September 2007: VERTEX07 – Tobias Golling – ATLAS Alignment Strategy Page 13

2002-01-20T22:50:45