Forward Tracking in the 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 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: probability Kalman Filter Prediction Filtering (Updating) Smoothing Superior to simple helix fitter Quality Indicator: 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 Frhwirth 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 probability distribution Number of integrated bunchcrossings Before Hopfiel Neural Network After Hopfield Neural Network tracks will Skip a layer Connect directly to the IP regions