1. Matthew Reid 1 st Year PhD University of Warwick 1

2.  Interested in pattern recognition for pixel detectors  New techniques that could be applicable to the upgrade  Aim– Increase the reconstruction efficiency of tracks in the Timepix test beam  Any questions just interrupt me! 2

3. 3 Reference cluster Cluster in detector i

4. Tree structure of the kNN algorithm 4

5. 5 SEED HIT Candidate hits after 1 st iteration Candidate hits after 2 nd iteration

6.  Example for k=3, projections in both xz- plane and yz-plane: (Note degeneracy) xz-plane yz-plane 6 z z y x SEED HIT Candidate hits 1 st iter Candidate hits 2 nd iter

7.  Kalman Filter gives best estimates for a linear system (see backup slides for detail)  Allows you to take into account multiple scattering based on Moliere formula for thick material to find better track fit  Measurement uncertainty based on pitch of pixel ~ 55µm 7

8. Example taken using: bin/tpanal –c cond/Alignment507.dat –z /afs/cern.ch/lhcb/group/vertex/vol7/Timepix/ZSData/Run507.root –n 9 MAX number of tracks with current algorithm is the plane with the lowest number of clusters?... 109? WRONG!! Detector PlaneNumber of Clusters C03W0015110 K05W0019109 D09W0015124 M06W0015111 I02W0019109 E05W0015113 DUT D04W0015 Bias the data set so ignore 8

9.  The maximum number of clusters depends on the alignment of detectors  Since the alignment is not perfect we are required to look in area shown in graph  Hence not all clusters can be used in the reconstruction. 9

10. 10 Detector PlaneNumber of ClustersNumber of Aligned Clusters C03W001511094 K05W0019109101 D09W001512493 M06W001511191 I02W001910995 E05W001511397 TOTAL676571

11. Original AlgorithmNew Algorithm Number of Tracks5175 Number of rejected tracks-16 Efficiency54%79% 0.0714282 ??1.3548 (0.0908276) Projection of Tracks in xz 11 z x z x y

12.  Residuals are of a comparable order to the original code giving ~6µm, over all planes (unfortunately) 12

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