Forward Tracking Forward Tracking in the in the ILD Detector ILD Detector

backporcupineAI and Robotics

Dec 1, 2013 (3 years and 6 months ago)

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Forward Tracking
Forward Tracking
in the
in the
ILD Detector
ILD Detector


ILC








the goal

Standalone track search for the FTDs
(Forward Tracking Disks)




Digitizer
Vertextion
Reconstruction
Track
Reconstruction


Why not combine with TPC tracking?


TPC


FTDs


Methods

Cellular Automaton

Kalman Filter

Hopfield Neural Network


The Cellular Automaton


The Cellular Automaton


It's about rules


IP


cell ( segment)


state 0










On the FTDs


The Hits


The Segments


Cellular Automaton


Clean Up


Longer Segments


Cellular Automaton


Clean Up


Track Candidates


Kalman Filter

Prediction → Filtering (Updating) → Smoothing

Superior to simple helix fitter

Quality Indicator:
χ
² probability




Kalman Filter

Prediction → Filtering (Updating) → Smoothing

Superior to simple helix fitter

Quality Indicator:
χ
² probability






Track Candidates


Kalman Filter


p > 0.005




The Hopfield Neural Network

Track ↔ Neuron

Goal: the global minimum






T=2
T=1
T=0




Background

Inner 2 disks are pixel detectors

How many bunch crossings?

100 BX * hit density
≈ 900 hits / pixel disk


Ghost hits

Outer 5 disks are strip detectors

Solution: shallow angle


At the moment

Conversion to new geometry (staggered petals)

Debugging and efficiency

Seperating core form implementation

Sensible use of Hopfield Neural Network

Robustification

Picking up hits from other places

More analysis

Individual steering for pattern recognition → xml file

Measure the improvement (old vs. new software)


Thanks to

Rudi Frühwirth and Winfried Mitaroff

Steve Aplin, Frank Gaede and Jan Engels

And Jakob Lettenbichler (Belle 2)


Thank you!


Back Up


Dependencies

FTD drivers and gear → at the moment in
between solution

The real background

Bad
χ
² probability distribution

Number of integrated bunchcrossings


0.880
0.900
0.920
0.940
0.960
0.980
1.000
1.020
0.000
0.200
0.400
0.600
0.800
1.000
1.200
Efficiency and Ghost Rate of Cellular Automaton
Efficiency
Ghostrate
Quality
Quantile Size of Cellular Automaton Criteria


0
20
40
60
80
100
120
0.00
200.00
400.00
600.00
800.00
1000.00
1200.00
1400.00
1600.00
time for reconstruction of 10 events [s]
ForwardTracking
SiliconTracking
integrated bunch crossings




Before Hopfiel Neural Network


After Hopfield Neural Network


tracks will

Skip a layer

Connect directly to the IP
0
10
20
30
40
50
60
70
63.2
20
12.3
4.4
0.04
layer 0
layer 1
layer 2
layer 3
layer 4


regions