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

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

Comments on Heavy Flavour Jet Tagging with ZVTOP in JAS3

LCFI Physics Meeting 8th March 2005

• Jet flavour tagging in JAS3• Use of neutral energy• Cosθ dependence• Primary vertex momentum• Conclusions

Dave Jackson

Jo Sarsam

For:

LCWS-05

nth March 2005

Stanford, California

This talk {

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

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

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 3rd 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

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

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

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 ?)

10

11