1. Forward Tracking in a Collider Detector AIDA WP-2 Meeting, 27.10.2011 Frühwirth, Glattauer, Mitaroff

2. The Goal Standalone track search and precision track fit in the Forward Region Pilot implementation in the ILD framework Marlin Future: generic tool (AIDA deliverable)

3. Pilot Implementation in Marlin As presented by Frank Gaede at the LCWS11

4. Implemented as a Marlin processor Interface to LCIO v.2 (event data model and persistency) Interface to GEAR (detector description) Uses the MarlinTrk interface to access a fitter (KalTest at the moment)

5. Methods Cellular Automaton (track search) Kalman Filter (quality indicator and cuts) Hopfield Neural Network (best subset)

6. The Cellular Automaton

7. Kalman Filter MarlinTrk → KalTest + KalDet MarlinTrk → other fitter Quality indicator: χ ² probability

8. The Hopfield Neural Network Quest for the best subset Final tracks must be compatible Track ↔ Neuron Goal: the global minimum

9. Background and Ghost Hits Background: Pair production, problem on Pixel Disks Ghost Hits: Problem on Si Strip Detectors

10. Current Status First version uses already all the methods described Currently testing and improving the code Investigating bottlenecks in performance ways to reduce combinatorial background causes for dismissed true tracks

11. Current Status (cont.) Search for more criteria that describe a track Analysis tools Step Analyser (path of the track) Criteria Analyser (best cuts for a criterion) True Track Analyser (behavior of the true tracks) Ways to handle kinks in tracks Adding flexibility, aiming at the generic tool

12. Outlook to the Future Coping with more complex detector set ups (e.g. petal FTDs) Tackling larger background Precision track fit: DAF, GSF Picking up hits from non-forward tracks (TPC) Way more analysis and modification for optimal performance and quality