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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
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Standalone track search for the FTDs (Forward Tracking Disks) the goal
Track Reconstruction Vertextion Reconstruction Digitizer
Methods • Cellular Automaton • Kalman Filter • Hopfield Neural Network
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
The Hopfield Neural Network • Track ↔ Neuron • Goal: the global minimum
T=0 T=1 T=2
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)