20 august 2001d0-germany meeting b identification frank filthaut university of nijmegen

20 August 2001 D0-Germany meeting

DD

B IDentification

Frank FilthautUniversity of Nijmegen


DD 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


DD 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)!


DD 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)


DD 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

(2) vs z


DD 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


DD• Example for high pT: ttbar sample pT

rel

• Example for low pT: J/ KS sample pTrel

Electron tag


DD• Performance for high-pT Z bb sample:

– Efficiency includes b e branching ratio– Background taken from same sample

• Efficiency as fct of pT

Electron tag

No PS match PS match req

Efficiency (%) 5.2 ± 0.8 4.8 ± 0.8

Fake rate (%) 1.1 ± 0.1 0.47 ± 0.07


DD 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



• 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!)



• 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)


DD 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


DD 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 offline code


DD 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

xx xcuds

x

jetbxpjetcxpnjetudsxpn

jetbxpL

)|( )|( )|(

)|(

P

PHxfHxp

H

H

1

)|()|(

If a value is found

otherwise

f(x|H) is distribution of variable x for hypothesis H

PH is probability to find a value for hypothesis H

NB issue of how to deal with“missing” data


DD 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


DD 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


DD• Jet algorithm dependence

– E resolution

• MC parentage– At moderate pT 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


DD 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


DD 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

20 august 2001d0-germany meeting b identification frank filthaut university of nijmegen

Documents

p e jet

p e e jet

high p t

low p t

lowp t b physics benchmarks

b physics studies

b physics group

p t rel of muon