1. LCFI Package and Flavour Tag @ 3TeV Tomáš Laštovička Institute of Physics AS CR CLIC WG3 Meeting 9/6/2010

2. Page  2 LCFI Package  Used for jet flavour tagging and secondary vertex reconstruction.  Topological vertex finder ZVRES.  Standard LCIO input/output –Marlin environment (used for both ILD/SiD)  Flavour tagging based on Neural Nets. –Combine several variables… Probability Tubes Vertex Function

3. Page  3 NN Input Flavour Discriminating Variables  There are 14 flavour discriminating variables R  - and Rz- significance for 2 tracks with the highest impact parameter significance in R  (“leading tracks”) Relative momenta of the leading tracks (relative to jet energy) Joint Probability in R  and Rz Decay length and decay length significance (relative to jet energy) Pt-corrected vertex mass Secondary vertex probability Relative total momentum of non-primary vertex tracks and their number  These inputs are re-normalised and transformed by tanh() - except joint and secondary vertex probabilities.  Tracks/vertices have to pass some minimal selection cuts.

4. Page  4 NN Input Flavour Discriminating Variables  Inputs are sent to 3 neural networks (8 inputs each) according to the number of secondary vertices found in a given jet –0 vertices: R  -, Rz- significance and momenta for 2 leading tracks Joint Probability (R , Rz) –1 vertex and >1 vertices: Decay length, decay length significance, pt-corrected vertex mass, Total momentum of non-primary vertex tracks and their number, Joint Probability (R , Rz), Secondary vertex probability  This is not a dogma, inputs can be added/removed –Requires some coding. –Studies better done outside the package (I fancy FANN package for this purpose).

5. Page  5 Input Variables – Additional Topics  Joint Probability Calculation –Estimated using fits to impact parameter distributions. –Might depend on detector geometry and sim/rec effects.  K s,  and conversion tagger –Part of the package, depends on detector geometry.  Cuts on tracks/vertices for NN Inputs –There is a number of parameters to tune up the package (see next slide).

6. Page  6 LCFI Package Optimisation  Optimisation is not only a matter of Neural Net retraining. The package has plenty of parameters: –Track selection params –ZVRES params –Flavour Tag params –Vertex Charge params

7. Page  7 Example 1 SiD FastMC Di-jets @ 500GeV ISR removed by M inv cut b-jets (red) c-jets (green) Light-jets (black) R  1 R  2 Z 1 JP R  JP Z M 1 M 2 DL SDL Pt CMRM #t V#V SVPE Z 2

8. Page  8 Further Examples  I compared various samples (sorry for too many plots).  Let’s start with the same setup but for 3 TeV –It’s pretty much similar as far as input variables are concerned.

9. Page  9 SiD FastMC Di-jets @ 3TeV ISR removed by M inv cut SiD FastMC Di-jets @ 500GeV ISR removed by M inv cut b-jets (red) c-jets (green) Light-jets (black) R  1R  2 Z 1Z 2 JP R  JP ZM 1M 2 DL SDLPt CMRM #t V#VSVPE

10. Page  10 Further Examples  I compared various samples (sorry for too many plots).  Let’s start with the same setup but for 3 TeV –It’s pretty much similar as far as input variables are concerned.  ff 2-jet events @ 3 TeV

11. Page  11 Di-jets @ 3TeV ISR removed by M inv cut ILD Full Sim/Rec ff @ 3TeV DST files area normalised M inv cut R  1R  2 Z 1Z 2 JP R  JP ZM 1M 2 DL SDLPt MCRM #t V#VSVPE b-jets (red) c-jets (green) Light-jets (black)

12. Page  12 Further Examples  I compared various samples (sorry for too many plots).  Let’s start with the same setup but for 3 TeV –It’s pretty much similar as far as input variables are concerned.  ff 2-jet events @ 3 TeV  H 0 A 0 4-jet events –First reconstructed with the SiD FastMC, –then with the full simulation and reconstruction. –Please, ignore c-jets.

13. Page  13 Di-jets @ 3TeV ISR removed by M inv cut SiD FastMC H 0 A 0 @ 3TeV no M inv cut 4 jet events area normalised b-jets (red) c-jets (green) Light-jets (black) b-jets (red) c-jets (green) Light-jets (black) R  1R  2 Z 1Z 2 JP R  JP ZM 1M 2 DL SDLPt MCRM #t V#VSVPE

14. Page  14 ILD Full Sim/Rec H 0 A 0 @ 3TeV DST files 224 – 231, 825-840 4 jet events area normalized R  1R  2 Z 1Z 2 JP R  JP ZM 1M 2 DL SDLPt MCRM #t V#VSVPE b-jets (red) c-jets (green) Light-jets (black) SiD FastMC H 0 A 0 @ 3TeV no M inv cut 4 jet events area normalised

15. Page  15 Discussion  SiD FastMC consistent for 500GeV and 3TeV. –And consistent to full SiD reconstruction @ 500GeV.  Then things get bit more complicated to compare –Different events, detectors, reconstruction, low statistics. –ff events comparable for b- and c-tag. Light jets probably polluted (?). –H 0 A 0 events: b-events more or less OK, however: Differences between FastMC and full simulation reconstruction (e.g. P t corrected mass  secondary vertex reconstruction?).  Different input distribution compared to the reference one  worse performance with default nets.

16. Summary LCFI package has a number of flavour tag sensitive variables, these can be revised/modified. We’ve looked at a couple of samples using SiD FastMC as well as DST files from Marco (full simulation and reconstruction). Future Plans: b-tag will be studied more closely. c- and uds- mistag efficiencies. Optimisation of the LCFI package.