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LCFI Package and Flavour Tag @ 3TeV

LCFI Package and Flavour Tag @ 3TeV. Tomáš Laštovička Institute of Physics AS CR CLIC WG3 Meeting 9/6/2010. LCFI Package. Used for jet flavour tagging and secondary vertex reconstruction. Topological vertex finder ZVRES. Standard LCIO input/output

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LCFI Package and Flavour Tag @ 3TeV

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

  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. NN Input Flavour Discriminating Variables • There are 14 flavour discriminating variables R- and Rz- significancefor 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. 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. Input Variables – Additional Topics • Joint Probability Calculation • Estimated using fits to impact parameter distributions. • Might depend on detector geometry and sim/rec effects. • Ks,  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. 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. Example 1 b-jets (red) c-jets (green) Light-jets (black) SiD FastMC Di-jets @ 500GeV ISR removed by Minvcut Z 1 Z 2 R 2 R 1 M 2 M 1 JP R JP Z DL S DL RM Pt CM #t V #V SVP E

  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. b-jets (red) c-jets (green) Light-jets (black) R 1 R 2 Z 1 Z 2 JP R JP Z M 1 M 2 SiD FastMC Di-jets @ 3TeV ISR removed by Minv cut DL S DL Pt CM RM #t V SVP E #V SiD FastMC Di-jets @ 500GeV ISR removed by Minv cut

  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. b-jets (red) c-jets (green) Light-jets (black) R 1 R 2 Z 1 Z 2 JP R JP Z M 1 M 2 Di-jets @ 3TeV ISR removed by Minv cut DL S DL Pt MC RM #t V SVP E #V ILD Full Sim/Rec ff @ 3TeV DST files area normalised Minv cut

  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 similaras far as input variables are concerned. • ff 2-jet events @ 3 TeV • H0A0 4-jet events • First reconstructed with the SiD FastMC, • then with the full simulation and reconstruction. • Please, ignore c-jets.

  13. b-jets (red) c-jets (green) Light-jets (black) b-jets (red) c-jets (green) Light-jets (black) R 1 R 2 Z 1 Z 2 JP R JP Z M 1 M 2 Di-jets @ 3TeV ISR removed by Minv cut DL S DL Pt MC RM #t V SVP E #V SiD FastMC H0A0@ 3TeV no Minv cut 4 jet events area normalised

  14. b-jets (red) c-jets (green) Light-jets (black) R 1 R 2 Z 1 Z 2 JP R JP Z M 1 M 2 SiD FastMC H0A0@ 3TeV no Minv cut 4 jet events area normalised DL S DL Pt MC RM #t V SVP E #V ILD Full Sim/Rec H0A0 @ 3TeV DST files 224 – 231, 825-840 4 jet events area normalized

  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 (?). • H0A0 events: b-events more or less OK, however: • Differences between FastMC and full simulation reconstruction (e.g. Pt 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.

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