1. 1 LCWS talks I Sat 19 March Tracking and Vertexing session: 09:00-09:15 Sonja Hillert (University of Oxford) Heavy Flavour ID and Quark Charge Measurement with an ILC Vertex Detector 09:15 - 09:30 Konstantin Stefanov (RAL) Linear Collider Flavour Identification 09:30 - 09:45 Andre Sopczak (Lancaster U) Charge Transfer Efficiency Studies of CCD Vertex Detectors 09:45 - 10:00 Yasuhiro Sugimoto(KEK) Fine Pixel CCD Option for the ILC Vertex Detector

2. 2 LCWS talks II Sat 19 March 2pm Simulation and Recon session: 15' Astrid Muennich TPC Simulation 15' Nikolai Sinev Track reconstruction efficiency using vertex detector as primary tracking device 15' David Jackson Jet Flavour Tagging using ZVTOP with JAS3 15' Bruce Schumm Measuring Forward Selectrons 15' W. Mitaroff The visualisation tool of the VERTIGO package 15' Dmitry Onoprienko Calorimeter-assisted tracking of long-lived particles with SiD. Mon 21 March 5pm Plenary session: Chris Damerell Vertex Detectors and the LC

3. 3 Comments on Heavy Flavour Jet Tagging with ZVTOP in JAS3 LCFI Physics Meeting 8 th March 2005 Jet flavour tagging in JAS3 Use of neutral energy Cosθ dependence Primary vertex momentum Conclusions Dave Jackson Jo Sarsam For: LCWS-05 n th March 2005 Stanford, California This talk {

4. 4 Polar angle dependence of jet flavour tag LCFI study with SGV for standard ILC vertex detector; track resolution: For a 1 GeV track at cosθ ~ 0.9 resolution is 3 times worse than at cosθ ~ 0.0. Study: Consider 45 GeV jets from heavy quark Z decays, |cosθ|<0.9 Set up 2-input b/c-jet flavour tag using neural network cjnn Look at tagging performance at large |cosθ| Can this be improved by tuning the tag in this region ?

5. 5 Two input flavour tag (as seen at SLD) PT corrected mass: Secondary vertex momentum vrs PT corrected mass: (These are the two inputs to cjnn)

6. 6 Flavour tag performance for 2 input network Applied to region |cosθ|>0.8 only Applied to region |cosθ|>0.8 only; but trained with |cosθ| as 3 rd input 2-input network trained and applied to |cosθ|>0.8 region only Conclusion: Tagging performance degraded at large |cosθ| Not easily recoverable by retraining neural network

7. 7 All ZVTOP based flavour tagging variables in the past have relied on properties of the secondary vertices. But there is also information in the primary vertex that is not 100% correlated with that in the secondaries. The summed momentum of tracks in the primary vertex differs for b-jets and c-jets due to differing fragmentation functions Motivates study of this variable as an additional neural network input

8. 8 Flavour tag performance for 2 input network Flavour tag performance for 3 input network Conclusion: Including the primary vertex momentum improves the b/c separation by ~10% (a big gain for such a straight forward input)

9. 9 Toshi Abe, LCWS02 NN inputs: vertex mass, vertex momentum, decay length, normalised decay length, no. of vertices found, no. of 1-prong vertices found (call ZVTOP twice with different RCUT/XCUT ?)

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