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Calibration of b-tagging at Tevatron

Calibration of b-tagging at Tevatron. A Secondary Vertex Tagger Primary and secondary vertex reconstruction Tagger characteristics Determination of ”b-tagging efficiency” (”c-tagging efficiency”) Determination of ”mistag rate” Systematics. 1. Secondary Vertex Tagger algorithm.

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Calibration of b-tagging at Tevatron

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  1. Calibration of b-tagging at Tevatron A Secondary Vertex Tagger Primary and secondary vertex reconstruction Tagger characteristics Determination of ”b-tagging efficiency” (”c-tagging efficiency”) Determination of ”mistag rate” Systematics CAT physics meeting 1

  2. Secondary Vertex Tagger algorithm • Explicitely reconstruct secondary vertices (other options are counting displaced tracks,...) • Used for most D top results B(t→Wb)~1 • Similar algorithm used by CDF • Requires the position of the primary interaction (primary vertex or PV) = Lxy C.Clément 2

  3. The Secondary Vertex Tagger Algorithm (SVT) Three steps • Reconstruction and identification of a primary vertex (PV) • Reconstruction of track based jets (”track-jets”) • Secondary vertex finding Step I: determine PV on a per-event basis • Fit all well reconstructed tracks to a common point of origin, • Remove tracks with too high 2contributions, • Repeat with remaining tracks, • Select main PV with pT distribution least consistent with min. bias (D), • Select main PV closest to high pT lepton or PV highest scalar sum of track pT (CDF). C.Clément 3

  4. Step II: track based jets or ”track-jets” • Pre-clustering: make precluster in z (along beam axis) of tracks that are nearby in z. Start from highest pT tracks. • Track selection: associate each precluster to the closest PV, use tracks that have pT>0.5 GeV, 1 hit in the most precise section of the silicon, small dca and zdca • From the preclusters, the tracks are clustered with a simple cone algorithm, with a track seed of pT>1 GeV. Track-jets useful in many other situations... C.Clément 4

  5. Step III: Secondary vertex finding • Start from seed vertices in each track-jet (i.e. all pairs of tracks) • Add tracks to seed vertices if there 2 contribution is not too large • Select vertices with 2 tracks, |Lxy|<2.6 cm (within first silicon layer!), Lxy > n (Lxy) , (adjust n to required rejection), 2,... (2 steps in CDF.) ”b-tagged” = there is 1 SV within R=0.5 of the calorimeter jet. C.Clément 5

  6. Tagger characteristics • Probability to tag a b-jet = ”b-tagging efficiency” • Probability to tag a light jet (g, u, d, s) ”mistag rate” • Probability to tag a c-jet ”c-tagging efficiency” These parameters are in general functions of the jet pT and , Could also be dependent on the PV position, the luminosity, run range ... C.Clément 6

  7. Probability to tag a b-jet To decouple from detector issues, define (CDF and D) • Taggable jets, (experiment wide definition) • Tagged jets A calorimeter jet is taggable if: • ET>15 GeV, ||<2.5, (i.e. Jet energy scale is defined!, detector dependent) • If it contains a track-jet within R<0.5 • Some quality requirements on the track-jet. Taggability: Later on, derived in 3 regions of zPV (D) # taggable jets (ET,) Taggability(ET,) = -------------------------- # jets(ET,) Different parameters In CDF C.Clément 7

  8. Taggability(ET) Taggability() Taggability Taggability must be derived from ”generic QCD” data Use same trigger than the signal sample, to incorporate luminosity, run number dependences... For example ttbar l+4 jet signature, take the events passing the lepton+1 jet trigger. Signal fraction is ~10-4 : so no bias. Compute taggability in bins of  and pT • Sample dependence: • Low MET passing EM trigger •  + jet + high MET sample Taggability(ET,)  k  Taggability(ET)  Taggability() C.Clément 8

  9. Heavy flavor  more tracks Taggability and jet flavor Taggability derived from data is valid for light flavor jets ONLY. Higher taggability for heavy flavor jets Derive correction from MC Cross check ratio of heavy-enhanced- to-light taggability in data and MC, agreement better than 2% level Taggability(ET,,flavor) in MC Ctaggabiliy(flavor) = --------------------------------------- Taggability(ET,,light jets) in MC # taggable jets (ET,) Taggability(ET,,flavor) = Ctaggability(flavor)  ------------------------------- # jets(ET,) C.Clément 9

  10. b-tagging efficiency # tagged jets (ET,) b(ET,) = -------------------------------------- # taggable jets (ET,) b-tagging efficiency is defined by Derive this quantity from data using a sample enhanced in heavy flavor. Typically back-to-back dijet events with various taggers: SVT, soft muon or electron tagger (D = a muon inside a jet with pTrel>0.7 GeV, CDF electron inside a jet) Method introduced in D by LEP folks... C.Clément 10

  11.   b-tagging efficiency from data   ”n-sample”: 1 jet with a   Not -tagged n -tagged n Not -tagged, SVT tagged nSVT -tagged, SVT tagged n ,SVT ”p-sample”: 2 b-2-b jets 1 jet with a    -tagged, SVT tagged p,SVT -tagged p Not -tagged, SVT tagged pSVT Not -tagged p C.Clément 11

  12. # events that are c- or light- jets Solve system of 8 equations, with 8 unknowns (in bins of  and pT) • Extract: sample composition and efficiency of the taggers • Makes a number of assumptions... → systematic errors b (ET,) efficiency of -tagger SVTb(ET,) efficiency of 2nd tagger SVT SVT SVT SVT b- contribution c/light contribution C.Clément 12

  13. System 8 assumptions and systematics ,SVT =c    SVT, assume c=1 (MC gives c=1.010.01) • Decorrelation of the 2 taggers: • Assume that the -tagger has same efficiency for c- and light-jets, ok because pTrel has similar shape for c- and light-jets at Tevatron energy. • Compare pTrel templates from several generators • Assume that c- and light-jet backgrounds can be lumped together, this is characterised by a factor  (varied for systematics) • Solve the system for various values of pTrel cut 0.3 - 1.5 GeV. • ~1 takes into account correlations b/w p and n samples (varied foe systematics) C.Clément 13

  14. b-tagging efficiency b→, data(ET,) From data we can only extract b-tagging efficiency for muonic b-jets We need the b-tagging efficiency for ”all kinds of b-jets” b(ET,) Pbtag(ET,) = b,MC(ET,) ------------------b→, data(ET,) Taggability(ET,)  Ctaggability(b) b→,MC(ET,) Transform semi-muonic b-tag efficiency into inclusive one C.Clément 14

  15. negative tags positive tags -n n Lxy/(Lxy) Mistag rate Extract from data as much as possible Similar approach in CDF and D The number of negative tags gives information on resolution effects Must take into account: 1) Long-lived particles in light jets are not completely removed by V0 filter: contribute to positive tags in light jets 2) Contamination of negative tags data by heavy flavor (2% b-jets and ~4% c-jets) # negatively tagged jets (ET,) _data(ET,) = ----------------------------------------- # taggable jets (ET,) CDF fits contributions to observed pseudo-c C.Clément 15

  16. _data(ET,) extracted in data passing e+jet trigger, with MissingET<10 GeV Validation: • Alternative parametrization derived from single electron trigger • Compare predicted and observed number of negative tags in high MissingET region Correct for long-lived particles in light-jet sample: Correct for the fraction of heavy flavor in the low MissingET electron sample SFll = #negative tags/#positive tags in light-flavor QCD Monte Carlo SFhf = #positive tag from light flavor / # positive tag from all flavors light(ET,) = _data(ET,)SFhf SFll C.Clément 16

  17. Negative tag rate validation Alternative parametrizations derived: - from single electron trigger - (instead of e+jets) Compare predicted and observed number of negative tags in high MissingET region C.Clément 17

  18. Systematic uncertainties • Taggability • Statistical error on parametrization from data • Variation on the parametrization by changing sample • Difference b/w predicted and observed # taggable jets at high Njet • Flavor dependence of taggability: MC dependence • b-tagging efficiency • Statistical error on semi-muonic b-tagging parametrization from data • System-8 assumptions • Ratio of semi-muonic to inclusive b-tagging efficiency in MC (statistical+sample dependence) • c-tagging efficiencies • Mistag rate • Negative tag rate, data statistics • Negative tag rate, sample dependance • Heavy flavor contamination • Negative to positive tag ratio for light flavor jets C.Clément 18

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