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B IDentification

B IDentification. Frank Filthaut University of Nijmegen. Goals. Basic goal: efficient b-tagging in both high-p T (Higgs, top, SUSY) and low-p T (B) physics Benchmarks set in Run 2 Workshops Higgs / Supersymmetry (’98) for high p T

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B IDentification

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  1. B IDentification Frank Filthaut University of Nijmegen D0-Germany meeting

  2. Goals • Basic goal: efficient b-tagging in both high-pT (Higgs, top, SUSY) and low-pT (B) physics • Benchmarks set in Run 2 Workshops • Higgs / Supersymmetry (’98) for high pT • Using secondary vertex tag and assuming “nominal” Run 2 detector performance, estimated close to 60% efficiency for mistag rate below 1% • B physics (’00) for low pT • More difficult to give a single number (trigger, analysis details) • Charge of the DØ b-id group: • Provide the physics groups with the algorithms and the tools to study their results, both off-line and (where relevant) at trigger level • Cooperate with physics groups in optimisation D0-Germany meeting

  3. Tags • Conceptually, all possibilities for tags exhausted (we think!): • Soft lepton (±, e±) tags • : Paul Balm (L3), Onne Peters • e: Abid Patwa, André Turcot (L3), Georg Steinbrück, Florian Beaudette, Jean-François Grivaz • Secondary vertex tag • Axel Naumann (L2), Arnaud Duperrin, Mossadek Talby, F. Villeneuve-Séguier (L3), Ariel Schwartzman, Marcel Vreeswijk • Impact parameter tag • Jon Hays, Ian Blackler (L3), Bram Wijngaarden, Frank Filthaut, Sasha Khanov, Flera Rizatdinova • Multivariate combinations of the above • Pavel Demine, Strasbourg (likelihood), Andy Haas (NN), Sherry Towers (guru) • Requires discriminating information from individual tags (rather than yes/no) • flavour tag • No manpower yet (may come from within B physics group) • Thought this was a pure B physics issue, but it turns out other groups also need this (e.g. t  tbar distinction) • In contrast, in Run I DØ used only its muon tags (J/ for B physics, inclusive semileptonic decays in general)! D0-Germany meeting

  4. Muon tag • L3: starting from previous L3 jet and muon “tools” • Associate muon with jet within some cone • Calculate pTrel of muon w.r.t. jet axis to distinguish between muons from b quarks and from ,K decays (and c quarks) • Using pT from muon chambers or central tracker? Resolution vs probability of wrong track – muon association • Effort not yet started (work on input L3 muons) • Offline: • Same variable, plus: P / Ejet , DCA (significance), z (significance) • +jet reco efficiency only ~ 50% for B physics (ttbar) D0-Germany meeting

  5. Electron tag • L3: effort mainly geared towards recognising J/ • B physics as well as low-energy calibration tool • Elements in common with generic L3 electron tag: electron recognition tools (track-CAL, track-PS, CAL-PS match) • Studies so far (April Vert Review): • MC-track match (R < 0.07) • Track-CAL match: • : 63 mrad (20 mrad core) • : 0.03 core but large tails (PV position!)  match in z! (changed) • CPS-CAL match: • : 29 mrad • : same PV tails • Track-CPS: • : 6.1  4.5 mrad • z: 10 mm  z  vs z  (2) D0-Germany meeting

  6. Electron tag • L3 cont’d: • Total e± tagging efficiency ~ 26% • Good for (part of) B physics studies • What about: • High pT? • Semileptonic decays? • FPS? • Offline: • Improved soft electron (E > 2 GeV/c) recognition using track extrapolation in CAL, reducing #cells taken into account • Still need PS match to reduce fake rate! • Variables: pTrel, pe/Ejet , Ejet, soft electron EEM/ptrack D0-Germany meeting

  7. Electron tag • Example for high pT: ttbar sample pTrel • Example for low pT: J/ KS sample pTrel D0-Germany meeting

  8. Electron tag • Performance for high-pT Z bb sample: • Efficiency includes b  e branching ratio • Background taken from same sample • Efficiency as fct of pT D0-Germany meeting

  9. Secondary vertex tag • L3: fast algorithm based on Hough transform • tracks in 2D space (r,  plane)  hits in 2D parametric space (d, 0) • In current implementation, start from tracks that have been found previously using a similar algorithm • but should be possible to use “official” L3 track reconstruction • Look for clustering in 0 coordinate, then “optimise” distance d • Problem: many PV tracks included in SV thus reconstructed (try to distinguish using 2 fit to either PV or SV, and cut on dt) • Intrinsic to method: binning not very fine • SV: require |d| > 1 mm, at least 3 tracks • All highly optimised for high-pT samples; 35% SV prob vs 10% PV prob D0-Germany meeting

  10. Secondary vertex tag • Offline: can do vertex finding in 3D: Kalman filter • Start by clustering tracks (simple cone, R = 0.5) • Build up SV starting from track pairs, reject tracks associated to PV and MB interactions; track pT and opening angle cuts • When SV found: associate with jet within R < 0.3 • Tag: Lxy/xy > 3 • Constrained fits  also track parameters improved • Works rather well for high-pT events (also optimised for ttbar!) D0-Germany meeting

  11. Secondary vertex tag • How well do things work for B physics? • Tracking efficiency in jets as fct of pT  down to 40% from tracking alone • Boost much smaller (<c> ~ 6 mm)  PV track rejection: 24% • After all cuts: efficiency ~ 15% Separate B physics selection required! Quality OK: resolution ~ 50 m (r,), 80 m (z) D0-Germany meeting

  12. Impact parameter tag • Offline: • Take collection of tracks • Select best PV based on z coordinates • Calculate each track’s impact parameter w.r.t. PV • Can be 2D (r- plane) or 3D • So far, studies have concentrated on 2D • Either cut on #tracks above given (physics-)signed i.p. significance, or multiply tracks’ PV probabilities to yield a discriminant (both possibilities implemented) • Need to reject tracks from , K (preferably explicitly) Z  bb ttbar(b) ttbar(l) Z light 2D impact parameter significance D0-Germany meeting

  13. Impact parameter tag • Copying CDF cuts: • 3 tracks with d/d > 2, or • 2 tracks with d/d > 3 • Starting effort on 3D tags • “Real” 3D: distance between track and PV, physics signed • Pseudo 3D: combining separate (r,) and (s, z) information (when useful) • Performance potentially more sensitive to luminosity • L3 effort has just started • Trying to re-use existing off- line code D0-Germany meeting

  14. Multivariate tags • Likelihood tag • Basic use: combination of independent 1D distributions • Higher dimensionality of the problem taken into account by doing this as a function of jet , pT • Also looking into 2D distributions • Variables used so far: pTrel,, Lxy/xy, mSV, charged energy fraction If a value is found NB issue of how to deal with “missing” data otherwise f(x|H) is distribution of variable x for hypothesis H PH is probability to find a value for hypothesis H D0-Germany meeting

  15. Multivariate tags • Results (for Z  bb vs Z  light quarks) • NN tag: using the same input, but (in principle) allows to consider full dimensionality of the problem. Started recently • Perhaps harder to understand  keep also likelihood method • NB: also individual tags can use neural nets (some do already) • NB: • 0.1 < efficiency < 0.4 • rejection > 0.992 D0-Germany meeting

  16. Common issues • Tracking efficiency in jets • Low even for MC • Luminosity dependence • Tracking efficiency • Vertex finding and selection • Jet direction (for pTrel) and energy (some criteria relative to Ejet) • Jet algorithm dependence • Cone vs. kT, algorithm parameters (so far we’ve used R=0.7 cones??) • Also: use of tracks during jet reco (instead of association afterwards) Cone jets kT jets D0-Germany meeting

  17. Common issues • Jet algorithm dependence • E resolution • MC parentage • At moderate pTjet ( ~ 50 GeV/c), large fraction of b jets originates from gluon splitting rather than lowest order production of b quarks • Makes definition of efficiency ambiguous • Lack of large (recent) MC samples of wide range of processes D0-Germany meeting

  18. Schedule • Presently, largest effort into understanding / improving performance on MC • Our inputs are also continuously changing • Takes time to find out and recover from • About to study effect of trigger • Was difficult so far, as there was no common n-tuple with both trigger and offline information • Should start trying to understand the quality of the data • Muon, dimuon, and muon+jet trigger exists now • Difficult, as b-ID is at the end of the food chain • Calorimetry, tracking, muons all need to work • Software: n-tuple, thumbnail support • Try to study / implement as much as possible of the triggers • Mainly muons • After shutdown (December), phase in other triggers • As soon as possible (allowing time for commissioning) • For our physics coordinator: first physics results by Moriond? • Is really pushing it D0-Germany meeting

  19. Conclusions • A fairly solid start has been made with b tagging • But much remains to be done • Our group is clearly manpower-limited • Algorithm development in the DØ environment is not very efficient • Especially if you’re “overseas” • DØ tends to “institutionalise” responsibilities • But one person’s effort cannot be spread too thin • Most of the people in the group are also working on other – and often more urgent – projects. • More than enough room to accommodate new collaborators D0-Germany meeting

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