Pattern Recognition in OPERA Tracking

1. Pattern Recognition in OPERA Tracking A.Chukanov, S.Dmitrievsky, Yu.Gornushkin JINR, Dubna OPERA collaboration meeting, Ankara, Turkey, 1-4 of April 2009

2. Outline • Problems of pattern recognition in OPERA tracking • Hough Transform method for straight track recognition • Simple Tracing method for curved track reconstruction • Spanning Tree method for curved tracks • Status of proposed pattern recognition package.

3. There is a problem in standard OpRelease pattern recognition algorithm. • Mushower doesn’t solve the problem but just serves as a patch for tracking algorithm. Mushower extrap Standard track Pictures from Dario’sreport 29/10/2008

4. Antoine’s presentation at LNGS end of 2008

5. A few examples from the latest RECO file

9. The OpRelease pattern recognition definitely needs to be improved. The efficiente pattern recognition method widely used in HEP experiments (e.g. MINOS, ALICE, CBM, etc) is Hough transform (HT) algorithm The algorithm is a part of the BrickFinder and is fully integrated in the OpRelease

10. Hough Transform for Straight Track Recognition Hough transform uses representation of a line in normal form: This equation specifies a line passing through point . That line is perpendicular to the line drawn from the origin to point in polar space. It can be shown that in case of points belonging to the same line and are constants. For each of given point iteration through different angles gives us corresponding values of . Points are saved in a 2D histogram. If there are some straight tracks (or parts of tracks) in an event there should exist distinct pikes in the histogram. By determining of centers of gravity of that pikes it is possible to reconstruct parameters of track lines by the following formulas:

11. Example of HT Track Recognition: event 234948251 OpRelease tracking: solid line - Kalman extrapolation, dash line - Mushower extrapolation. Proposed HT track finding Found and give track parameters:

12. Example of HT Track Recognition: event 234643825 OpRelease tracking: solid line - Kalman extrapolation, dash line - Mushower extrapolation. Proposed HT track finding Found and give track parameters:

13. Example of HT Track Recognition: event 234655944 OpRelease tracking: solid line - Kalman extrapolation, dash line - Mushower extrapolation. Proposed HT track finding Found and give track parameters:

14. Example of HT Track Recognition: event 234862308 OpRelease tracking: solid line - Kalman extrapolation, dash line - Mushower extrapolation. Proposed HT track finding Found and give track parameters:

15. Example of HT Track Recognition: event 234917207 OpRelease tracking: solid line - Kalman extrapolation, dash line - Mushower extrapolation. Proposed HT track finding Found and give track parameters:

16. As shown in the given examples the muon track is unambiguously distinguished by a pike in HT histogram in case of so called difficult events. Moreover, the result of Hough transform coincides with Mushower extrapolation already at the pattern recognition level (without a fit). With ~300 events with a muon found in the CS (from Giovanni) a general performance was estimated

21. There are still events with muon track not found in the CS (out of list of Giovanni) (badly reconstructed in std tracking procedure?)

23. Houph Transform reconstruction of the same event

25. Houph Transform reconstruction of the same event

26. But sometimes due to low momentum of the particle it is really difficult

27. Simple Tracing Method for Curved Track Finding After the initial straight part of a track is determined by a Hough transform in the beginning of an event it is possible to find the rest tail part of the track with help of proposed tracing method (which in fact is a simplified kind of a Kalman filter): 1) Finding a search direction Linear fit on 7 last found hits of a track; 2) Setting of search angle range Its own angle range for each detector is used taking into account its geometry and uncertainties. 3) Finding hits in the following detector planes inside the search angle range Inefficiency of detectors(3 empty TT planes, 11 empty RPC planes)is taken into account. If there are more than 1 candidates to track hits only the hit accepted that is the nearest to the search direction. 4) Including found hit to the TrackElement and iterating steps 1-3 for next planes or stop procedure in case of no hits found

28. Backward Tracing with Background When particle’s momentum is small the track can be curved already in its beginning part. The curved tracks are difficult for HT reconstruction and even the simple tracing method can fail within the shower environment. On the picture below sucha specific case is shown. Event 217982179 Y, cm TT planes Line found by a Hough transform track hits wrong hits There are no more sequential hits in the search area Z, cm

29. Example of Forward Tracing Procedure Simple tracing along the beam direction works easily (as shown on the picture) because there are no background hits far away of the vertex. Event 23356121 Y, cm TT1 RPC2 TT2 RPC1 Z, cm

30. Spanning Tree Tracing Method for Track Selection To solve such a problem it is useful to iterate on all possible chains of the hits to select among them the best chain. It can be done with help of method of spanning tree tracing. It finds different reliable track trajectories and than consider the longest and most smooth chain of hits to be the best track candidate. Event 217982179 Y, cm TT planes As a result the longest and most smooth track will be selected Z, cm

31. Status of Proposed Pattern Recognition Package • Event cleaning (removing of CT and isolated hits): • done • 2) Method of Hough Transform to find straight part of a track: • done • 3) Tracing method to find curved tail part of a track: • done • 4) Method of Spanning Tree Tracing to select the best track candidate within a shower: • done