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Jet Tagging Studies at TeV LC

Jet Tagging Studies at TeV LC. Tomáš Laštovička, University of Oxford Linear Collider Physics/Detector Meeting 14/9/2009 CERN. Overview. LCFI Package Monte Carlo Samples Jet Tag Performance and Comparisons Neural Net Inputs and Optimisation Physics Analyses Summary. LCFI Package.

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Jet Tagging Studies at TeV LC

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  1. Jet Tagging Studies at TeV LC Tomáš Laštovička, University of Oxford Linear Collider Physics/Detector Meeting 14/9/2009 CERN

  2. Overview • LCFI Package • Monte Carlo Samples • Jet Tag Performance and Comparisons • Neural Net Inputs and Optimisation • Physics Analyses • Summary

  3. 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 (more details later) Probability Tubes Vertex Function

  4. Monte Carlo Samples • Generated with CalcHEP 2.5.j • 500GeV, 1TeV, 2TeV, 3TeV center of mass energy • di-jets (e+e- → qqbar) with ISR, no beamstrahlung • Decayed and fragmented with Pythia 6.4.10 • 50k events for b,c and {u,d,s} • event weights are accounted for • s-channel events frequently intensively “boosted” along z-axis due to high energy ISR • radiativereturn to the Z-peak • ~80% of s-channel b-events: Z-peak 1TeV 2TeV 3TeV

  5. Monte Carlo Samples • Events passed to FastMC using both SiD (sid02) and CLIC (clic01_sid)geometries (a minor problem with clic01_sid in FastMC solved). • LCFI package run locally in Oxford (2x4x3x50k = 1.2M events in about 2 days) 3TeV bB-event vtx+tracker

  6. 500GeV SiD • A comparison to the previous (LoI) SiD result done with full digitisation/simulation and beamstrahlung effects. • Neural Nets need to be retrained (see dashed (default NNs) vs full line) • b-tag slightly better while c-tag is slightly worse, generally reasonable agreement. Fast MC, no beamstrahlung SiD LoI, full sim/dig/rec c (b-bkgr)‏ b c

  7. 3TeV SiD vs CLIC Geometry • Purity vs efficiency is not the best plot to look at, it involves cross sections and various acceptance effects. • Nevertheless, here is a comparison of SiD and CLIC geometry at 3 TeV. • SiD geometry over-performs CLIC geometry due to better resolution (especially where light quarks are involved). • At 3TeV more b-quarks decay after 15mm (1st SiD vtx layer) – minor effect if compared to the resolution. c (b-bkgr)‏ b c

  8. Mistag Efficiency vs b-tag Efficiency • Mistag efficiency vs tag efficiency are less affected by cross sections/acceptance effects. • B-tag example (dashed = default b-tag net, full line = retrained) • This net was trained to separate b jets from both light and c-jets, not against c-jets only 3TeV CLIC 500GeV SiD Effect of NN re-training Significantly better performance The exact understanding of this difference is unclear at this stage.

  9. Decay Vertices of B-mesons • B-mesons are significantly more boosted at 3TeV. • And decay further from the interaction point. • In central region, about 9.5% of B0s decay after 15mm (1st SiD layer) and 5.5% after 30mm @ 3TeV. Vertex detector Central barrel 1TeV 2TeV 3TeV

  10. Neural Net Inputs • LCFI package classifies jets in one of three Neural Nets based on #vtx • 1 vertex (only IP vertex), 8 Inputs • R-Phi and Z significance of 2 leading tracks and their momenta • Joint R-Phi and Z Probability • 2 vertices • Decay Length and its significance • Pt mass correction • Raw momentum • Number of tracks in vertices • Secondary vtx probability • Joint R-Phi and Z Probability • 3+ vertices, NN Inputs just as for the 2 vertices but separate NN.

  11. Neural Net Inputs 500GeV - SiD

  12. Neural Net Inputs 3TeV – CLIC Geometry

  13. Important Neural Net Inputs • Most relevant NN inputs are • joint probabilities in both R-Phi and Z coordinates • Pt mass corrections for cases with secondary vertices. • Also secondary vertex probability, raw vertex momentum etc.

  14. 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

  15. Physics Analyses I • Higgs branching ratios – Higgs-strahlung • H→ccbar analysis was a part of SiD/ILD LoI • Leading to BR uncertainty of about 8.5%. • High quality c-tagging required and advanced analysis. SiD

  16. Physics Analyses – Higgs Self-Coupling with ZHH • Uncertainty on about 30% level claimed for 500 GeV ILC and 2000 fb-1 • We could not reproduce this result due to: • Final state gluon radiation → • It was OFF in many previous studies • Degrades results by a factor of 2. • Real detector response and full set of backgrounds • Further degradation to 100+ % • If a self-coupling measurement is suppose to support the LC case, more work is definitely required. • At 3TeV advantage of higher luminosity, W channel.

  17. Summary We have analysed FastMC so far: experience from LoI tells us that the full simulation and reconstruction is essential as well as a full inclusion of the beam backgrounds. LCFI package should be optimised for 3TeV CLIC, Neural Nets retrained. At this stage, 3TeV CLIC b- and c- tagging is not yet fully understood. We will need to decide what approach to take for the CDR in 2010.

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