forward tracking in the ild detector
DESCRIPTION
Forward Tracking in the ILD Detector. ILC. Standalone track search for the FTDs (Forward Tracking Disks). the goal. Track Reconstruction. Vertextion Reconstruction. Digitizer. Why not combine with TPC tracking?. TPC. FTDs. Methods. Cellular Automaton Kalman Filter - PowerPoint PPT PresentationTRANSCRIPT
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Forward Tracking Forward Tracking in the in the
ILD DetectorILD Detector
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ILC
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the goal
Standalone track search for the FTDs (Forward Tracking Disks)
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Digitizer VertextionReconstruction
TrackReconstruction
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Why not combine with TPC tracking?
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TPC
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FTDs
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Methods
Cellular Automaton
Kalman Filter
Hopfield Neural Network
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The Cellular Automaton
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The Cellular Automaton
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It's about rules
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IP
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cell ( segment)
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state 0
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On the FTDs
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The Hits
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The Segments
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Cellular Automaton
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Clean Up
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Longer Segments
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Cellular Automaton
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Clean Up
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Track Candidates
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Kalman Filter
Prediction → Filtering (Updating) → Smoothing
Superior to simple helix fitter
Quality Indicator: χ ² probability
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Kalman Filter
Prediction → Filtering (Updating) → Smoothing
Superior to simple helix fitter
Quality Indicator: χ ² probability
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Track Candidates
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Kalman Filter
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p > 0.005
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The Hopfield Neural Network Track ↔ Neuron Goal: the global minimum
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T=2
T=1T=0
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Background Inner 2 disks are pixel detectors How many bunch crossings? 100 BX * hit density ≈ 900 hits / pixel disk
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Ghost hits
Outer 5 disks are strip detectors Solution: shallow angle
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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)
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Thanks to
Rudi Frühwirth and Winfried Mitaroff Steve Aplin, Frank Gaede and Jan Engels And Jakob Lettenbichler (Belle 2)
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Thank you!
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Back Up
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Dependencies
FTD drivers and gear → at the moment in between solution
The real background Bad χ ² probability distribution Number of integrated bunchcrossings
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0.880 0.900 0.920 0.940 0.960 0.980 1.000 1.0200.000
0.200
0.400
0.600
0.800
1.000
1.200
Efficiency and Ghost Rate of Cellular Automaton
EfficiencyGhostrateQuality
Quantile Size of Cellular Automaton Criteria
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0 20 40 60 80 100 1200.00
200.00
400.00
600.00
800.00
1000.00
1200.00
1400.00
1600.00
time for reconstruction of 10 events [s]
ForwardTrackingSiliconTracking
integrated bunch crossings
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Before Hopfiel Neural Network
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After Hopfield Neural Network
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tracks will
Skip a layer Connect directly to the IP
0
10
20
30
40
50
60
7063.2
20
12.3
4.40.04
layer 0 layer 1 layer 2 layer 3 layer 4
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regions
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