B Tagging in Jets at Hadron Colliders

1. B Tagging in Jets at Hadron Colliders Matthew Jones Purdue University

2. Disclaimer • Results are primarily from the CDF experiment • Most apply to high energyjets as in Z, H, t decay • Performance limited by detector capabilities... • New LHC experiments work exceptionally well! ANY MATERIALS ARE PROVIDED ON AN "AS IS" BASIS. MATTHEW JONES SPECIFICALLY DISCLAIMS ALL EXPRESS, STATUTORY, OR IMPLIED WARRANTIES RELATING TO THESE MATERIALS, INCLUDING BUT NOT LIMITED TO THOSE CONCERNING MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE, SUCH AS APPLICATION IN A HEAVY ION EXPERIMENT, OR NON-INFRINGEMENT OF ANY THIRD-PARTY RIGHTS REGARDING THE MATERIALS. VOID WHERE PROHIBITED BY LAW.

3. Typical Applications • Cleanest environment at LEP, • measurement of • Electroweak couplings via AbFB • search for • At the Tevatron, • b production cross sections • reconstruction of top decays, tWb • Higgs searches, • Single top production • Typical jet energies are ET > 50 GeV • These analyses generally require well-understood b-tag efficiencies and fake rates.

4. Properties of B Hadrons B hadrons have unique properties because: • The b quark mass is large, • high multiplicity of decay products • decay products can have a hard momentum spectrum • The CKM matrix element |Vcb| is small, • Relatively long lifetime,

5. B Production in collisions

6. Modeling B Production • Accurate modeling of B production and decay is an essential tool • Allows development and tuning of b tagging algorithms • First-order acceptance/efficiency estimates • While important, these models are never perfect • Unknown contributions to inclusive B decays • Uncertain production mechanisms and pT spectrum • Detector effects not accurately modeled • Corrections to efficiency must be measured using data • Fake rate difficult to model accurately and must also be measured.

7. Monte Carlo Event Generators • Hard process: • Event generation (eg, Pythia, Herwig) • Structure functions, matrix element, parton shower • Explicit calculations of are less applicable • Fragmentation function, • Include some description of the underlying event • B decay generators: • Model much of what we have learned about B decays from ARGUS, CLEO, BaBar, Belle, ... • CLEO developed QQ, later interfaced with event generators at CDF in Run I • Now superseded by EvtGen...

8. EvtGen B Decay Generator • Provides a convenient framework for modeling a wide variety of decays • Efficient decay chain simulation via helicity amplitude formalism • Decay table: • Only about half of the decay width is accounted for in exclusive final states. • Naive spectator model invoked for B0s and Λb decays. • The remaining inclusive decays are simulated using a variant of the Lund string fragmentation model. • Generally good agreement with observed B decay properties...

9. Inclusive processes Lepton momentum in semi-leptonic decays EvtGen B Decay Generator

10. Leptons (e or μ), Displaced tracks: significantly non-zero impact parameter reconstructed secondary vertices approximately Lorentz invariant Properties of B Decays

11. The CDF Detector Muon systems: CMP CMX CMU Hadronic EM Calorimeter TOF SVX-II COT

12. CDF II Calorimeter PMT’s Light guides Pb-Scintillator EM section Fe-Scintillator Hadronic section

13. Small cell drift chamber in a 1.4 Tesla field 4 axial, 4 stereo superlayers, 12 sense wires per layer Fast input to level 1 track trigger Finds tracks with pT>1.5 GeV/c Extrapolates to EM calorimeter and muon chambers Highest quality tracks found the tracker, extrapolated into the silicon detector. CDF II Tracker

14. 5 layers of double- sided sensors: 3 φ-z (90°) 2 φ-SAS (±1.2°) 1 single-sided inner layer attached to beam pipe Not a pixel detector: most accurate reconstruction is in the r-φ plane. CDF II Silicon Detector 10.6 cm 90 cm Luminous region: σz ~ 30 cm

15. Iterative cone algorithm, typically using Jet Reconstruction • Corrections applied for • Non-uniformity in η • Event pileup: 350 MeV in cone per additional vertex • Non-linear tower response • Unrecognizable as jets until ET > 20 GeV • Associate tracks that lie within ΔR < 0.4 ~ η φ

16. Secondary Vertex Tagging Algorithm SecVtx algorithm: Phys. Rev. D71, 052003 (2005). • Applied to tracks with pT>0.5 GeV/c in a cone around a jet within ΔR < 0.4. • Find all tracks with impact parameter significance, • Fit a vertex to all pairs of tracks • Associate other tracks if • Refit all tracks to common vertex • Remove tracks with large • Require significant displacement, • Lxy > 0: dominated by b-jets • Lxy < 0: mis-tagged jets

17. Secondary Vertex Tagging Algorithm • Second pass: • If no vertex found in pass 1, search lower-quality two-track vertex using higher pT tracks with |d0/σd0|>3. • Different operating points: • Tight (as described) • Ultra-tight (without lower-quality second pass) • Loose (relaxed impact parameter significance cuts, additional attempts to seed pass 1 vertices)

18. Heavy flavor jets: vertices with positive 2d decay length Light flavor jets: equal numbers of positive and negative 2d decay length vertices not quite... correct for: K0S and Λ decays nuclear interactions

19. Measuring B Tag Efficiencies • In principle, this is trivial in Monte Carlo • In practice: • Apply same B tag algorithm to data and Monte Carlo • Correct the efficiency in Monte Carlo using scale factors: • Efficiency measured in data: • Select a sample of jets with enhanced b fraction • Measure the efficiency and heavy flavor fraction simultaneously

20. Measuring Efficiency with Leptons • Efficiency measured in data: • Tag one jet with a high pT muon • Tag opposite side jet with positive SecVtx tag • Require Mvtx > 1.5 GeV/c2 to suppress light flavor and charm • Fit for heavy flavor fraction using lepton pT,rel Example:

21. Measuring Efficiency with Electrons • 8 GeV electron trigger sample enriched in semi-leptonic B decays • Apply tag to away-side jet • Naive efficiency for positive tagged electron jet: • Subtract expected light-flavor mis-tags:

22. Measuring Efficiency with Electrons • Heavy flavor fraction of away side jet: Light jet fraction Probability of tagging light away-side jet From Monte Carlo Measured using yield of e+D0 estimated using a sample enriched in photon conversions (mostly light flavor)

23. SecVtx Tag Efficiency • Efficiency calculated for b-jets in a Monte Carlo sample of top decays • Scale factor applied to give efficiency in data.

24. Fake Rates • Probability that a light jet is tagged as a B jet • To first order, a generic jet sample is mostly light flavor • Measure negative tag rate as a function of: ETjet,ΣETjet, |η|, NZ vtx,Zpv • Typically of order 1%... • But this needs to be corrected for: • NLF+ > NLF- due to K0S and Λ decays: α • Generic jet sample contains heavy flavor: β

25. Tag rate asymmetry: α correction • Definition: • Application: • Fit bottom, charm, light flavor fractions using templates constructed from • Signed vertex mass, Mvtx • Pseudo-proper time, • Typical result: α ~ 1.2 – 1.5 (function of ET)

26. Tag rate asymmetry: β correction • Definition: • Application: • Fit the flavor fractions in the pre-tagged samples: β ~ 1.1

27. Fake Rates • Higher fake rates when track occupancy is high (high ET jets) and near edge of tracking acceptance.

28. Jet Probability Algorithm • Jet axis used to construct signed impact parameter for high quality tracks in a jet: • Impact parameter significance: Likely to be positive for tracks from displaced secondary vertices Symmetric about zero for tracks from the primary vertex. Phys. Rev. D74, 072006 (2006).

29. Signed impact parameter significance... Negative side fitted with resolution function R(S). Track probability: uniformly distributed between 0 and 1 for prompt tracks. Jet probability: Jet Probability Algorithm Uniformly distributed between 0 and 1 for light flavor jets.

30. Jet Probability Algorithm • The efficiency/purity can be continuously adjusted by selecting PJ < PJcut. • Typical operating points: PJ < 1% or PJ < 5%. CDF 50 GeV Jet sample Monte Carlo

31. Jet Probability Efficiency/Fake Rate Comparable to SECVTX performance but not completely correlated.

32. Neural Networks • The SecVtx algorithm presumably does not use up all available information • Further discrimination possible using advanced multivariate methods (eg, ANN’s) • Train network using signal, background from mistags • 31 variables had at least 3.5σ discriminating power used for input • # tracks with d0 significance > 3, • signed d0 significance of tracks • vertex mass • and many others...

33. Neural Networks • Network output is insensitive to the origin of the B jet • Corrections calculated for sample dependence for mistagged jets

34. Summary • Characteristic features of heavy flavor decays exploited in various ways to tag B jets • An extremely valuable element: • Ability to estimate light-flavor contamination using negative decay length/impact parameter jets • Even this is not ideal, but can be improved by α,β corrections • Neural networks can be useful • provided they don’t sacrifice the ability to measure efficiency and fake rates in data • Hopefully, some of these ideas can be translated to an environment with lower ET and higher track multiplicity • high quality 3d tracking using pixel detectors may be key