b-tagging activities aug 9, 2007physics.bu.edu/~narain/temp/btag-mn-aug2007-lpc.pdf · b-tagging...
TRANSCRIPT
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b-tagging activitiesAug 9, 2007
Meenakshi NarainBrown University
(co-conveners of LPC btag:Gerber & Narain)
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July 2007 Workshop @LPCGoals and Format• Goal:
– General review of b tagging and vertexing– Strategies and plans for how to measure performance
with real data.• Format:
– Presentations in the morning with afternoon fordiscussions & decisions
• Topics:– Monday: Vertexing and btagging– Tuesday: How to measure efficiency and mistags from
data– Wednesday: 1) How to use the measurements from
data in our physics analyses and 2) effect of detectorissues on performance of btagging
– Thursday: Trigger and Wrapup
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Documentation and Infos• b tag & vertex algorithm task lists & contacts on
twiki page– https://twiki.cern.ch/twiki/bin/view/CMS/BtagPOG .
• LPC btag workshop page:– Comprehensive summary of various activities
– http://indico.cern.ch/conferenceDisplay.py?confId=15416
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Btagging/Vertexing Algos• Many algorithms exist and implemented in
CMSSW• Performance being optimized• Validation suites being developed
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Thomas Speer 9th July 2007 - p. 2
VertexReconstruction
• Vertex Reconstruction:
Vertex Finding: Identification of vertices and assignment of tracks tovertices, with possible estimate of vertex position
Offline primary vertex reconstruction
Vertex finding in Jets
Vertex Fitting: Most precise estimate of the vertex position and trackparameters at vertex from a set of tracks
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Thomas Speer 9th July 2007 - p. 3
VertexReconstruction
• Vertex Reconstruction:
Vertex Finding: Identification of vertices and assignment of tracks tovertices, with possible estimate of vertex position
Offline primary vertex reconstruction
Vertex finding in Jets
Vertex Fitting: Most precise estimate of the vertex position and trackparameters at vertex from a set of tracks
• Vertices and b-tagging:
Primary Vertex:
determine origin of jet - fragmentation tracks originate from the PV
impact parameters, flight distances, etc., are defined relative to PV
Secondary vertex reconstruction
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Thomas Speer 9th July 2007 - p. 4
VertexReconstruction
• Vertex Reconstruction:
Vertex Finding: Identification of vertices and assignment of tracks tovertices, with possible estimate of vertex position
Offline primary vertex reconstruction
Vertex finding in Jets
Vertex Fitting: Most precise estimate of the vertex position and trackparameters at vertex from a set of tracks
• Vertices and b-tagging:
Primary Vertex:
determine origin of jet - fragmentation tracks originate from the PV
impact parameters, flight distances, etc., are defined relative to PV
Secondary vertex reconstruction
• Description of the algorithms
• Description of the VertexReco framework
• To-do list !
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Thomas Speer 9th July 2007 - p. 5
In the beginning were the tracks...
• Persistent track: reco::Track in DataFormats/TrackReco
• States stored:
“Initial State”:
For the primary tracks: 2D-PCA to beamline
For the other tracks, where it makes the most sense
E.g., for vertex constrained tracks, at the vertex
On First and Last measurement layer
For all states: (x, p) + curvilinear error (21 floats)
• Not suitable for most higher-level algorithms (e.g. vertex, b/ -tagging)
no access to magnetic field (no propagation!)
use Tracks through TransientTrack
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Thomas Speer 9th July 2007 - p. 6
TransientTrack
• Transient track: reco::TransientTrack (in TrackingTools/TransientTrack)
https://twiki.cern.ch/twiki/bin/view/CMS/SWGuideTransientTracks
• Gives access to different states, etchttp://cmsdoc.cern.ch/Releases/CMSSW/latest_nightly/doc/html/dd/dc7/classreco_1_1TransientTrack.html
New: state at PCA to arbitrary BeamLine, taking into account tilt. (e.g. forTIP w.r.t. to be helix-line PCA)
Has access to magnetic field
• ReferenceCounted (à la TSOS)
Different concrete classes: TrackTransientTrack, GsfTransientTrack,TransientTrackFromFTS
Same interface
• In your application, build TT through TransientTrackBuilder:
//get the builder from the EventSetup:
edm::ESHandle<TransientTrackBuilder> theB;
iSetup.get<TransientTrackRecord>().get("TransientTrackBuilder",theB);
//do the conversion:
vector<TransientTrack> t_tks = (*theB).build(trackCollection);
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Thomas Speer 9th July 2007 - p. 7
Algorithms
• VertexFitters:
https://twiki.cern.ch/twiki/bin/view/CMS/SWGuideVertexFitting
Kalman Filter
Adaptive Vertex Fitter
TrimmedKalmanVertex Fitter
Gaussian-Sum Filter
and others, not ported to CMSSW: Least Trimmed Squares, Least Medianof Squares, Minimum Volume Ellipsoid, Minimum Covariance Determinant,M-estimator ...
• Vertex finders:
https://twiki.cern.ch/twiki/bin/view/CMS/SWGuideOfflineSecondaryVertexFinding
TrimmedKalmanVertexFinder
AdaptiveVertexReconstructor
MultiVertexFit
TertiaryTracksVertexFinder
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B-tag introduction
Different b-tagging algorithms may have different features in term of performances (efficiency vs mis-tagging rate) robustness against detector effect (e.g.
misalignment, tracker inefficiencies) possibility to measure its efficiency on data need for MC calibration, data only calibration
or no calibration
So, different analysis may want to use different algorithms
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JetProb
JetTracksAssociation
Structure of b-tagging
The CMS b-tagging is now organized as a two phases process:
first some “tag info” or “tagging variables” are computed for jet/tracks/vertices/leptons
then the computed information are used to compute the “discriminators” (floats) that can be used in the analysis
RECO
TagInfos
Discriminators(produced with a pluggable fwk)
TracksJets
Calo,PF,GenPrimaryVertex
Muondata
ECALdata
ImpactParameter● IP 3D and 2D● dLen, jetDist●Track prob
CombinedSV● Secondary Vtx● multiplicity, mass● flight dist,...
SoftLepton (X2)● Lepton ID● Ptrel, Lepton IP● energy fraction,..
HighEffTkCntHighPur
TkCnt
CombSV
MVASV
MVAIP
Softele
Soft mu noIP
Softmu
New1 New2New3
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Lifetime based algorithms
Algorithms in CMS exploiting lifetime: Combined Secondary Vertex Track Counting Jet probability
Pixel detector needed for all lifetime algorithms pixel resolution ~50um SiStrip only resolution ~mm
Track quality selection is also applied to reject tracks with badly measured impact parameters
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Combined SV algorithm In CMS a “combined” algorithm based on SV is
avaiable:
Define 3 vertex categories: reco vertex, pseudo vertex, no vertex
Computes in each case some vertex/jet properties such as:
track multiplicity invariant mass decay length (in transverse plane) track rapidities (wrt jet direction) fraction of energy of the SV IP of first track above charm
A likelihood function is used to combine the above information
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CombinedSV variables
FINAL DISCRIMINATOR
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Soft lepton tagging
A b-hadron can decay producing one or more lepton in three ways: direct decay b -> l- (BR 10%) via charm, b -> c -> l+ (BR 8%) via anti charm, b -> cbar -> l- (1.6%)
The main background for this algorithm are light meson decaying to leptons, photon conversion, and wrong lepton ID
The Pt_rel and the IP of the lepton are used to increase the discriminating power
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Performances
Track CountingCombinedSVProbabilityMVA IPMVA SV
CMSSW
ORCA / PTDR
Tk CountingHigh Eff
Jet ProbabilityCombined SV
Tools exists in RecoBTag/Analysis to study algorithm performance in a standard way
The performances of the algorithms in CMSSW is almost at the level of PTDR
Training/calibration still needed to get optimal performances
MVA very preliminary but promising
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Vertexing/btagging US tasks– Improve analysis / validation suite
• F.Yumiceva, V.Bazterra, C.Kopecky, L. Christofek, PuertoReco (E.Ramirez et al.).
– Provide ultra-combined (MVA-based) b tag(L.Christofek, in collaboration with C.Saout).
– Make use of Muon ID default in b µ tag, toimprove performance at low Pt. (Ping Tan)
– Check if Track (HitPattern) RECO/AOD objectcontains all info we need for b tagging (Z.Wan).
– Investigate use of track jets and DR association withCaloJets. (C.Gerber).
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b tag performance w/ data– Use of b mu to measure b efficiency
(Ping/Gerber & Francisco/Narain/Bloch)
– Use of –ve tags to measure uds efficiency(L.Christofek, Jeremy & Daniel)
– Use of t-t-bar to measure b and c efficiency(Kukartsev , Narain, Speer, Joris & Steven)
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Methods for Performance Studiesusing data
• btag efficiency from ttbar events– (Santa Barbara, Bruxelles)– Use b-enriched sample of semileptonic ttbar events to estimate
btag eff.• SystemD method
– (FNAL, Brown, Strasbourg)– Use muon+jet events and two ~uncorrelated taggers to measure
the b-tagging efficiency.• Pt-rel method
– (UIC, FNAL)– Measure tagging eff. of lifetime based taggers using pt-rel
distribution in muon+jet events• Light quark mistagging rate from data
– (Strasbourg)– Use –ve impact parameter significance distribution in data to
estimate light quark mistag rate
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using µ+jet events ‘System 8’ Method• Method requires events with 2 jets, one with a muon of
Pt > 6 GeV.– Make 8 measurements:
• µ+jets, µ+ jets tagged with lifetime,• µ+ jets tagged with pT(rel); µ+ jets tagged with both.• Repeat requiring away jet tagged by lifetime.
• Then solve for unknowns !
b-lifetime tag efficencyb µ pt_rel efficency
Measured& true
efficiencies
D.Bloch, M.Narain, F.Yumiceva
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‘System 8’ Method• Expected performance in early running:• Use “µ in jet trigger”
• Back-of-the-envelope calculation– (M.Narain, D. Bloch, F. Yumiceva):
– Relative systematic errors: ~10% at 10 pb-1 & ~3% for > 100pb-1.
• Relative Statistical errors:
1 fb-1
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pT-rel Method• Use µ+jet events• Determine b-fraction using a fit of templates to the muon pt-rel
distribution
• Extract btag efficiency from above fractions determined before andafter applying other (lifetime based) tagger
P. Tan, C. Gerber
( ) ( )relccrelbb pTfNpTfN !+!
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Tagging efficiency and ttbar cross section with semileptonic decaysJuly 30, 2007, Gena Kukartsev Slide 5 of 12
The method
L=−log Poisson N 1 ,N 1×Poisson N 2 ,N 2×Poisson N 3 ,N 3
July 30, 2007, Gena Kukartsev Slide 5 of 10
● We use semileptonic decays:
● From data:
– N1, N2, N3 - number of events with 1,2,3 tagged jets
– Luminosity
● From MC:
– Fijk – fractions of events with “i” b-jets, “j” c-jets, “k ” light jets (no tagging, MC truth only)
– Selection efficiency sel
● We expect <N1,2,3> = f( b, c, l, Fijk, sel, lumi, ttbar )
● Maximize loglikelihood and find b, c, ttbar:
–
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Tagging efficiency and ttbar cross section with semileptonic decaysJuly 30, 2007, Gena Kukartsev Slide 9 of 12
Toy result
Discriminator value
b
lc
Solid lines – true MC values
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Tagging efficiency and ttbar cross section with semileptonic decaysJuly 30, 2007, Gena Kukartsev Slide 10 of 12
Confidence levels
Monte Carlo (equivalent ~ 100/pb)
# tags # events0 16911 42562 28063 3784 205 1
68% confidence level
95% confidence level
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User Interface• Software Design (V.Bazterra/ Thomas Speer)
– Will create DB DB contains TRF for DATA and MCwith effi_b and effi_uds
– Will measure b, c & uds efficiency in data for 4 cuts(according to b tag efficiency) and for all b tagalgorithms.
• Results stored in DB, as function of Et, rapidity …• Also store b tag cut value used.
• User interface:– On data:
• bool pass = bTag(“Combined”, “Loose”, Jet);– On MC - use scale factor
• pair (pass, weight) = bTag(“Combined”, “Loose”, Jet, Truth);– Or on MC - use data TRF
• float effi = bTag(“Combined”, “Loose”, Jet, Truth);
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Tag Rate Functions• Assume the following are measured and available in data
for use:
• b-tag efficiency (TRF εb)– derived using ttbar events or muon-jet events or a combination
thereof.• c-tag efficiency (TRF εc )
– Derived from ttbar events or c-jet MC scaled to dijet datarates εc = εc
MC (εb→µdata/ εb→µ
MC)• Light quark Mistag Rates (TRF lq)
– derived using negative tags using multijet events + MCcorrection factors
– Or maybe smarter method at a future date which uses all tags
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TRFsTRFb TRFc
TRF light
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Multiple Operating Points
• AT CMS - if we want ALL btaggers for ALL jetdefinitions, we have at least 70 combinations!!!
• Need to think very carefully how to usebtagging in any physics analysis– if performance measurement is given from DATA
and NOT purely MC based.– Agreed on 4 operating points per tagger.
12 at Dzero
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Multiple Operating PointsExample from Dzero
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Tagging Analysis
Apply Selection Cuts
Apply b-tagging
Final DataSample
Apply Selection Cuts
Calculating b-taggingprobability
Background/Signal
Estimation
Data MC Background/Signal
Analysisapplies b-
tagging twoways…
Driven bymeasuring
efficiency ondata, not MC!!
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Estimating The Signal/Background
Measure b-tagging efficiency on data, but wish to applyin MC or other non-b-quark data sample.
Method 1 Determine efficiency vs. jet pT, η, etc., on data, anduse as lookup table in MC.
Method 2 Determine MC-to-data tagging ratio vs. jet pT, η, etc.,on data, and use as lookup table applied to tags found inMC.Both require determining, in Data, the eff of astandard b-jet and matching it to MC jets
Method 3 Use data sample with same flavor content of sampleyou are interested in, and derive tagging function (pT, η, etc.) and apply directly.
D∅(most analyses)
(hbb/D∅)
CDF/D∅
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Jet Tagging Probability
• For the MC event:– probability for a jet of a given flavor α (b, c or
light jet) to be tagged• product of the taggability and the tagging efficiency
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Event Tagging Probability
• For the MC event:• Event tagging probabilities Pevent:
– derived by weighting each reconstructed jet inthe event by the per jet tagging probability Pα(pT , η) according to its flavor α, its pT and its η.
• The probability to have at least one tag in agiven event
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Using Multiple Operating points• In the case that the working points are inclusive,
– i.e. a Tight jet is necessarily Loose.– A jet can be defined as :
• Tight tagged (T),• Loose but not Tight tagged (L)• not Loose tagged (U).
– The probability of an event to pass a given taggingscheme is given by:
– where the sum is over all permutations of T, L & U
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Tag Permutations
• The weighting procedure allow to estimatethe number of tagged events, but does notgive access to the actual tagged jets in theevent.
• If one wants to use kinematic variablesusing the tagged or untagged jets, then weneed to consider each permutation in thesum separately.
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Conclusions• Many US participants now plugged into
mainstream issues in btagging/vertexing
• A successful workshop with a lot of discussionwith all key developers of btagging– Many issues - mostly emphasizing how to measure
performance from data and how to use them inphysics analyses were discussed.
• This led to change in thinking of the group and henceagreement for possible modifications of the taggers to allowthis
• Develop Framework to measure performance from Data• Start a dialogue with the physics groups on proposal for
using btagging in the analyses (plus develop framework)