CVT Alignment Using a Kalman-Filter

Sebouh Paul

November 13, 2010

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Introduction

I One of the major obstacles to CVT calibration is thealignment.

I The CVT (central vertex tracker) consists of 84 silicon + 18micromega sensors.

I With 3 translation and 3 rotation degrees of freedom permodule, there are 612 alignment parameters.

I We plan to use existing code from CERN in order to run ouralignment https://kalmanalignment.hepforge.org/

I Our goal to use this program for alignment of the Clas12 CVTmodules.

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The Kalman-Filter Alignment (KFA) ProgramI Advertised as generic code that could be used for any tracker

I Requires a small set of matrices for each measured track.I Contains a test suite for aligning MC of a fictitious detectorI Program has not been updated since 2011.I Goal: revive this program and use it for the CLAS12 CVT,

starting with the SVT, later expanding to BMT.

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before alignment (Mean 1.9)

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The Kalman-Filter Alignment (KFA) ProgramI Advertised as generic code that could be used for any trackerI Requires a small set of matrices for each measured track.

I Contains a test suite for aligning MC of a fictitious detectorI Program has not been updated since 2011.I Goal: revive this program and use it for the CLAS12 CVT,

starting with the SVT, later expanding to BMT.

0 2 4 6 8 10 12 14 16 18 20/ndof2χ

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100

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500

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900

num

ber

of tr

acks

/ndof2χDistribution of track

before alignment (Mean 1.9)

after alignment (Mean 1.0)

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The Kalman-Filter Alignment (KFA) ProgramI Advertised as generic code that could be used for any trackerI Requires a small set of matrices for each measured track.I Contains a test suite for aligning MC of a fictitious detector

I Program has not been updated since 2011.I Goal: revive this program and use it for the CLAS12 CVT,

starting with the SVT, later expanding to BMT.

0 2 4 6 8 10 12 14 16 18 20/ndof2χ

0

100

200

300

400

500

600

700

800

900

num

ber

of tr

acks

/ndof2χDistribution of track

before alignment (Mean 1.9)

after alignment (Mean 1.0)

3 / 16

The Kalman-Filter Alignment (KFA) ProgramI Advertised as generic code that could be used for any trackerI Requires a small set of matrices for each measured track.I Contains a test suite for aligning MC of a fictitious detectorI Program has not been updated since 2011.

I Goal: revive this program and use it for the CLAS12 CVT,starting with the SVT, later expanding to BMT.

0 2 4 6 8 10 12 14 16 18 20/ndof2χ

0

100

200

300

400

500

600

700

800

900

num

ber

of tr

acks

/ndof2χDistribution of track

before alignment (Mean 1.9)

after alignment (Mean 1.0)

3 / 16

The Kalman-Filter Alignment (KFA) ProgramI Advertised as generic code that could be used for any trackerI Requires a small set of matrices for each measured track.I Contains a test suite for aligning MC of a fictitious detectorI Program has not been updated since 2011.I Goal: revive this program and use it for the CLAS12 CVT,

starting with the SVT, later expanding to BMT.

0 2 4 6 8 10 12 14 16 18 20/ndof2χ

0

100

200

300

400

500

600

700

800

900

num

ber

of tr

acks

/ndof2χDistribution of track

before alignment (Mean 1.9)

after alignment (Mean 1.0)

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Roadmap to AlignmentI Choose datasets: cosmic rays, and field-off tracks from target.

I Perform straight-tracks CVT reconstruction.I Create matrix-extraction service and run it on CVT data.I Run KFA.I Add alignment parameters to the CCDB.

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Roadmap to AlignmentI Choose datasets: cosmic rays, and field-off tracks from target.I Perform straight-tracks CVT reconstruction.

I Create matrix-extraction service and run it on CVT data.I Run KFA.I Add alignment parameters to the CCDB.

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Roadmap to AlignmentI Choose datasets: cosmic rays, and field-off tracks from target.I Perform straight-tracks CVT reconstruction.I Create matrix-extraction service and run it on CVT data.

I Run KFA.I Add alignment parameters to the CCDB.

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Roadmap to AlignmentI Choose datasets: cosmic rays, and field-off tracks from target.I Perform straight-tracks CVT reconstruction.I Create matrix-extraction service and run it on CVT data.I Run KFA.

I Add alignment parameters to the CCDB.

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Roadmap to AlignmentI Choose datasets: cosmic rays, and field-off tracks from target.I Perform straight-tracks CVT reconstruction.I Create matrix-extraction service and run it on CVT data.I Run KFA.I Add alignment parameters to the CCDB.

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Datasets to use

COSMIC FIELD-OFF

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Matrix-Extraction Service

I I have written code to extract matrices from reconstructedtracks.

I These matrices will be illustrated in the next few slidesI measurementI covarianceI extrapolationI alignment derivativesI track derivatives

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Measurements, covariance, and extrapolation

I ~m = measurement

I V = covariance

I ~c = extrapolation

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Alignment Derivatives

I Derivatives of expected hit position with respect to alignmentvariables (translations and rotations in x , y , z)

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Track Derivatives

I Derivatives of the expected hit position with respect to trackparameters: d0, φ0, z0, tan θdip

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CVT Alignment Status

I Matrix extraction implemented for SVT for translations androtations in x, y, z.

I Ran matrix extraction, and KFA for translations in x, y,replaced alignment values in local database, repeated severaltimes.

I Planned future workI database should have separate alignment params for

top/bottom layers in each SVT moduleI run KFA with translations in z and rotations in all three

directions.I add BMT to list of alignables.

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Results: Shifts

FIRST ITERATION

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Results: ResidualsBEFORE AFTER

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Results: χ2/ndof (first iteration)

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Results: χ2/ndof (after several iterations)

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Summary

I KFA is advertised as a generalized algorithm that can be usedon any tracker

I Preliminary results from KFA on the CVT appear promisingI residuals are centered at zeroI χ2/ndof is reduced.

I However, more work needs to be done.I Add multiple scattering to model of covariance.I Include BMT to alignablesI Incoporate both cosmics and field-off eventsI Extract all alignment variables.

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Backup slide: χ2/ndof from Monte Carlo

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