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
Forward Tracking Forward Tracking in the in the
ILD DetectorILD Detector
ILC
the goal
Standalone track search for the FTDs (Forward Tracking Disks)
Digitizer VertextionReconstruction
TrackReconstruction
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=1T=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 1.0200.000
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
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
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
7063.2
20
12.3
4.40.04
layer 0 layer 1 layer 2 layer 3 layer 4
regions
