1. A Pattern Recognition Scheme for Large Curvature Circular Tracks and Its FPGA Implementation Example Using Hash Sorter Jinyuan Wu and Z. Shi Fermi National Accelerator Laboratory, Batavia, IL 60510, USA

2. Soft Tracks? Can we always anticipate high-p T signatures? –Probably not. Do soft tracks carry useful information? –Maybe. In strong magnetic fields, (e.g. 4T in CMS), high- p T tracks look soft. Isn’t it too hard to use soft tracks in trigger stage? –It was, but now it is not too hard.

3. A Large Curvature Track A soft track hits large  region. –A global algorithm is better suited. The “high-p T ” approximation is not valid globally. –Exact track equation is needed. R r 00   Parameter: Initial angle Measure the tangent angle.. Parameter: Radius of curvature

4. Tangent Angle Measurements There are various techniques to measure the tangent angle of the track segment (or “doublet”, or “cluster”). Extra “ghost” segments may exist. The ghost segments may be resolved in track recognition process later. 

5. Simulation: Events Simulation is done in a CMS-like detector. Tracks are generated with flat distributions on initial angles and curvatures. Minimum radius of curvature of the tracks: 55 cm – barely hit outer most super layer of the detector. Number of tracks: 100 to 1000/event. (200 is shown.)

6. Simulation: Detector Hits Charged tracks hit 5 super layers of silicon detectors. Each super layer contains 2 layers. The hits on the 2 layers are used to measure the tangent angles of the tracks.

7. An Example of Track Recognition: Event We explain the track recognition process using this 20-track example.

8. An Example of Track Recognition: Hits Tracks generate hits on the detector with 5 super layers. Hits in the two silicon detector layers form “doublets”…

9. An Example of Track Recognition: Doublets Hits are paired together as doublet. Ghost doublets may exist.

10. An Example of Track Recognition: Histogram 00 c0c0 Tow track parameters can be calculated for each doublet. A 2-D histogram is booked. Doublets from same track are entered into same bin, (since they have same track parameters). Sometimes they are stored in clusters. This is a “ghost”.

11. An Example of Track Recognition: Clustering 00 c0c0 clustering The doublets in clusters are grouped together. The “ghost” doublets are gone. The 9-bin schemeThe 4-bin scheme For doublets on the seeding super layer in this bin… search for coincident in these 9 bins. For doublets on the seeding super layer in this bin… search for coincident in these 4 bins.

12. An Example of Track Recognition: Tracks All doublets from a track are contained in a cluster.

13. Before Full Track Recognition Two track parameters can be calculated for each doublet. Useful trigger primitives can be found without full track recognition. For example…

14. Example: Finding “Soft Jets” A simulated event with 200 tracks. Flat distributions. Min. R = 55 cm 16 soft tracks are added. They are grouped in 2 small initial angle regions, i.e., 2 “soft jets”. 00 00 Can you see the “soft jets”? Can you see the “soft jets” now?

15. The End Back Ups …

16. Large Curvature Tracks A soft track hits large  region. –A global algorithm is better suited. The “high-p T ” approximation is not valid globally. –Exact track equation is needed. R r 00  

17. Simulation: Detector Hits Charged tracks hit 5 super layers of silicon detector. Each super layer contains 2 layers. The hits on the 2 layers are used to measure the tangent angles of the tracks.

18. Hits