Tracking from raw data to analysis
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Tracking: From Raw Data to Analysis. Matthew Herndon, May 2006 University of Wisconsin CDF Silicon Detector Workshop. Raw Data and Calibration Clusters Hits, Resolution Tracking Physics Conclusion. BEACH 04. J. Piedra. 1. Raw Data. Bit 0 Bit 1 Bit ff, 7f Dup Chan 33 types.

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Tracking from raw data to analysis

Tracking: From Raw Data to Analysis

Matthew Herndon, May 2006

University of Wisconsin

CDF Silicon Detector Workshop

  • Raw Data and Calibration

  • Clusters Hits, Resolution

  • Tracking

  • Physics

  • Conclusion

BEACH 04

J. Piedra

1


Raw data

Raw Data

Bit 0

Bit 1

Bit ff, 7f

Dup Chan

33 types

Rick Snider, Matt Herndon, Lester Miller

Cannot abandon nearest neighbour readout

  • Organized by HDI, Chip and Strips

  • Interpreted by bank unpacker

  • Bit errors common from optical readout

    • Chip ID errors, Strip ID errors

    • Dropped readout

    • Errors in termination characters

  • Improved version: NN(nearest neighbour) bank unpacker

    • Chip ID errors and termination characters corrected by understanding readout order

    • Nearest neighbour readout mode allows correction of strip IDs from context

    • Bits 0-1, lowest significance bits correctable: higher bits correctible if lower bits look correct

  • Large amount of data corrected

    • Efficiency improved by a few %, number of bad clusters reduced

2


Calibration

Calibration

Jason Nielsen

SICHIPPED, SICHIPON, SISTRIPDH PROD_PHYS_SVX

Small residual pedestal: Dynamic Pedestal Subtraction(DPS)

  • Operational requirements

    • Calibration needed on short time scale(24 hours): Before Production calibration jobs

    • Production calibration jobs include beamline determination using tracking

  • Calibration

    • Calibrate per strip pedestals, noise, and identify bad strips(hot or dead)

    • Provide short time scale detector performance evaluation along with SVXMon

  • Procedure

    • Take SVXCAL run with DPS-on and DPS-off

    • Integrate calibration run information with dead strip list and create final tables

    • Other calibration runs: bias scans/gain scans performed by experts

  • Calibration benefits

    • Improves cluster resolution and identifies clusters with potentially bad resolution

    • Used in tracking efficiency measurements and realistic detector simulation SiExpected and bad strips

M. Herndon

3


Clustering

Clustering

Rick Snider, Matt Herndon

Doug Glenzinski

  • Clustering algorithm

    • Thresholds: 1 strip 4, 2 strips 3 and 2, 3 strips all 2

    • Loose clustering requirements for high efficiency

  • Clusters

    • Typically two strips: ~1/2 each

    • Charge varies between types/sides of sensors

  • Online Thresholds

    • Needs to be efficient for Landau peak of ~25ADC and RMS of order 5 counts

    • 7-10 ADC conservative

    • >12 ADC aggressive

4


The integration gate

The Integration Gate

Matt Herndon, David Stuart

R-phi,SAS, Z

  • Detector needs to be timed in

    • Integration window set relative to beam crossing time

    • Done for each type of sensor

    • Found that optimum window was different for different types of sensors

    • No optimum choice: May have changed with integrated radiation

M. Herndon

5


Cluster resolution

Cluster Resolution

Aaron Dominguez

  • Measuring intrinsic resolution

    • Constrain track to hits on two layers around layer of interest

    • Calculate intersection point using hit positions and transverse momentum of track

    • Measure resolution using likelihood fit

    • Found resolution had a considerable tail

    • Dropped entries outside of 2.5 to measure core

  • Track Fit

    • Tails still a problem for the track fit

    • Toy MC with % in tails reproduces misestimates of resolution seen in track fit

    • Double Gaussian model for track fit would be too time consuming

M. Herndon

6


Alignment

Alignment

Ray Culbertson

ISL: Juan Pablo Fernandez

L00: Ray, Aart Heijboer

  • Alignment Methodology

    • Start from survey results: extremely important as a starting point, crosscheck or constraint. Compare to survey at every step

    • First step global alignment SVXII, ISL, L00 - compare COT and silicon beamlines

    • Fix tracks to layer 5 and beamline and align inner layers

    • Align r-phi, SAS and then Z

    • Ailgn for bows and small rotations

    • Constrain/realign using overlap regions

  • ISL and L00

    • Separate process starting with OI tracks after SVXII alignment

M. Herndon

7


Tracking overview

Tracking Overview

  • Uses a general outside-in methodology

    • Start where evidence of tracks is most clear

    • Highest angular resolution portions of detector

    • Outer layers of COT and silicon

  • Algorithms

    • Form track seeds in outer layers

    • Helix from outer hits and beamline or vertex

    • Look for consistent hits moving inward

    • Refit and repeat after adding each hit

    • This method used for COT, OI(COT to silicon outside-in) and silicon only algorithms

M. Herndon

8


Oi tracking efficiency

OI Tracking Efficiency

Matt Herndon

Very low and went down with pT and time!

  • Measurement of the efficiency of adding silicon hits to COT tracks

    • Use J/ψ→ μ+μ- data

(2001-2002 data)

(2001-2002 data)

  • Problem is with COT alignment

    • Pointing very precise at high pt unless the alignment is not perfect

  • εSVX = 74.5 ± 0.3(stat) ± 2.2(sys)%(2001-2003 data)

    • Average efficiency for adding silicon to two tracks: In silicon fid.

M. Herndon

9


New oi efficiency

New OI Efficiency

old avg (88%)

w/ same data

(2003)

The Silicon Group

Matt Herndon

+2.5% and flat - improved code

+6% εSVX improved code and new quality cuts

  • Improved silicon pattern recognition code and detector performance

    • Hold search window open to at least 1mm until first silicon hit found

εSVX = 74.5% → 88.5%

2001-2003 → 2001-2005 data

M. Herndon

10


Silicon only tracking

Silicon Only Tracking

80%

TDR

Stephanie Menzemer, Thorsten Schiedle, Matt Herndon

  • Silicon Standalone Tracking: SiSA

  • Algorithm

    • Use two outer 3D point(r-phi + SAS) and vertex to find initial helix. Gives helix and constraint in z.

    • ISL cooling problems, wire bond oscillation, AVDD2 chip failures, beam incidents effect the stereo side disproportionately

    • SiSA Performance is suboptimal

M. Herndon

11


Silicon forward tracking

Silicon Forward Tracking

Z->ee MC

45%

85%

70%

  • Also new forward COT segment based algorithm

    • Combination will be very powerful

Matt Herndon, Thorsten Schiedle

Antonio Boveia, David Stuart

Realistic MC predicts efficiency well

  • New forward silicon algorithms

    • Use 1 outer 3D point(r-phi + SAS) and vertex to find initial helix. No constraint

    • Much higher potential performance

M. Herndon

12


L3 tracking

L3 Tracking

Dynamic Pedestal Sub(DPS) very effective

Matt Herndon

  • Algorithm to confirm L2 SVT decisions

  • Reduces output from L3

    • Often a limiting factor - Consumer Server Logger

  • Requirements - Fast

  • Solution

    • Only perform OI tracking

    • Apply no calibration (calibration people were unhappy)

    • Don’t read unnecessary code: Reading L00 or forward ISL

    • Profile all code and optimize anything that seems slow

  • Level 3 tracking

    • Executes in 1/5 the time

    • Loose a few % in efficiency and resolution

M. Herndon

13


Tracking from raw data to analysis

L00

Matt Herndon, Aart Heijboer

Auke Pieter Colijn, Chris Hill, David Stuart

Tim Nelson

  • Makes a huge difference in B physics

  • Data not usable as is

    • Large coherent noise: Continuous pattern across all strips on a sensor

    • Induced by silicon readout: Different event by event

    • Coupling between cables and shields and due to space constraint

  • Pedestal fit

    • Event by event fit to find the pedestal distribution

    • Ignoring sharp peaks: real clusters

    • Effectively finds clusters

    • CPU intensive: can’t be used in L3

    • Clusters after pedestal fit good for physics

    • Using tight quality cuts, but probably not necessary

  • Special track fit routine and alignment

14


Physics impact

Physics Impact

The B Group

3D

2D

Requires Z

  • Z strips and L00

  • Bhh

    • Two tracks always meet in 2D. 3D needed to constrain vertex

  • Bs 

    • 3D pointing to vertex

    • Use of L00

M. Herndon

15


B s mixing

Bs Mixing

Adding L00

Improvement in sensitivity at high ms comes from improvement in vertex resolution

The BsMixing Group

Sensitivity at 25ps-1

|Vtd| / |Vts| =

0.208 +0.008

-0.007

  • L00/forward tracking

  • Bs mixing

    • L00 improves proper time resolution

    • Sensitivity dependent on the exponential of the square of t

    • 30% muon tags from forward tracking - most powerful tag

M. Herndon

16


Conclusions

Conclusions

A/A (17.25 ps-1) = 3.5

  • Highly successful silicon tracker

  • All aspects of the system making a difference in physics analysis

    • SVXII gives high efficiency tracking and good vertexing for all analysis

    • ISL forward tracking: Seeded tracking used for Z and electroweak physics standard forward tracking for B physics

    • Z strips reduce background in B physics two track modes

    • L00 and SVT critical for Bs mixing

  • Many things could still be improved

    • Use of L00

    • Forward tracking

    • Tracking at high Luminosity

    • Faster code

    • See Rainer and Mircea

M. Herndon

17


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