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20 August 2001D0-Germany meeting B IDentification Frank Filthaut University of Nijmegen.

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Presentation on theme: "20 August 2001D0-Germany meeting B IDentification Frank Filthaut University of Nijmegen."— Presentation transcript:

1 20 August 2001D0-Germany meeting B IDentification Frank Filthaut University of Nijmegen

2 20 August 2001D0-Germany meeting 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 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 p T 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

3 20 August 2001D0-Germany meeting 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)!

4 20 August 2001D0-Germany meeting Muon tag L3: starting from previous L3 jet and muon “tools” –Associate muon with jet within some cone –Calculate p T rel of muon w.r.t. jet axis to distinguish between muons from b quarks and from ,K decays (and c quarks) Using p T 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  / E jet, DCA (significance),  z (significance) –  +jet reco efficiency only ~ 50% for B physics (ttbar)

5 20 August 2001D0-Germany meeting 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z  (2)  vs  z

6 20 August 2001D0-Germany meeting Electron tag L3 cont’d: –Total e ± tagging efficiency ~ 26% –Good for (part of) B physics studies –What about: High p T ? 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: p T rel, p e /E jet, E jet, soft electron E EM /p track

7 20 August 2001D0-Germany meeting Example for high p T : ttbar sample p T rel Example for low p T : J/  K S sample p T rel Electron tag

8 20 August 2001D0-Germany meeting Performance for high-p T Z  bb sample: –Efficiency includes b  e branching ratio –Background taken from same sample Efficiency as fct of p T Electron tag No PS matchPS match req Efficiency (%)5.2 ± 0.84.8 ± 0.8 Fake rate (%)1.1 ± 0.10.47 ± 0.07

9 20 August 2001D0-Germany meeting 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 d t ) Intrinsic to method: binning not very fine –SV: require |d| > 1 mm, at least 3 tracks –All highly optimised for high-p T samples; 35% SV prob vs 10% PV prob

10 20 August 2001D0-Germany meeting 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 p T and opening angle cuts –When SV found: associate with jet within  R < 0.3 –Tag: L xy /  xy > 3 –Constrained fits  also track parameters improved –Works rather well for high-p T events (also optimised for ttbar!)

11 20 August 2001D0-Germany meeting Secondary vertex tag How well do things work for B physics? –Tracking efficiency in jets as fct of p T  down to 40% from tracking alone –Boost much smaller ( ~ 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)

12 20 August 2001D0-Germany meeting 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 Z  light ttbar(b) ttbar(l) 2D impact parameter significance

13 20 August 2001D0-Germany meeting 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

14 20 August 2001D0-Germany meeting 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 , p T –Also looking into 2D distributions –Variables used so far: p T rel, , L xy /  xy, m SV, charged energy fraction If a value is found otherwise f(x|H) is distribution of variable x for hypothesis H P H is probability to find a value for hypothesis H NB issue of how to deal with “missing” data

15 20 August 2001D0-Germany meeting 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

16 20 August 2001D0-Germany meeting Common issues Tracking efficiency in jets –Low even for MC Luminosity dependence –Tracking efficiency –Vertex finding and selection –Jet direction (for p T rel ) and energy (some criteria relative to E jet ) Jet algorithm dependence –Cone vs. k T, algorithm parameters (so far we’ve used R=0.7 cones??) –Also: use of tracks during jet reco (instead of association afterwards) Cone jetsk T jets

17 20 August 2001D0-Germany meeting Jet algorithm dependence –E resolution MC parentage –At moderate p T jet ( ~ 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 Common issues

18 20 August 2001D0-Germany meeting 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

19 20 August 2001D0-Germany meeting 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

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