B Tagging with CMS Fabrizio Palla INFN Pisa B Workshop Helsinki 29 May – 1 June 2002

b b Tagging with CMSTagging with CMS

Fabrizio PallaFabrizio Palla

INFN PisaINFN Pisa

BB Workshop Workshop

HelsinkiHelsinki

29 May – 1 June 200229 May – 1 June 2002

B WorkshopHelsinki 2002

b Tagging with CMS Fabrizio PallaINFN Pisa

OutlineOutline

• Introduction• Impact parameter based tags• Secondary vertex based tags• Multi-jet studies• Trigger studies

IntroductionIntroduction

•Lot of B hadrons in the final state from interesting physic processes– Top – Higgs– Supersymmetry

•B tag relies upon the long lifetime and large mass

IntroductionIntroduction

•Example:Example:Effects on h bb decay reconstruction in MSUGRA

The problem definition The problem definition How a “real” 2-

jet event looks like:

Tags IngredientsTags Ingredients

1. Track reconstruction2. Transverse and longitudinal impact

parameter 3. Primary vertex reconstruction in z4. Jet reconstruction5. Vertex reconstruction

Impact parameterImpact parameter

• Linearise track @ point of Linearise track @ point of closest approachclosest approach

• Sign positive if the track-jet Sign positive if the track-jet crossing point is crossing point is downstreamdownstream

•NeedNeed•Jet•Primary vertex•Tracks

Track decay length

Track Track ReconstructionReconstruction

5 Efficiency for particles in a 0.4 cone around jet axis ET = 200 GeV Fake Rate < 8 *10-3

ET = 50 GeV Fake Rate < 10 -2

Primary vertex Primary vertex reconstructionreconstruction

• Track seeding findingTrack seeding finding– Hits in the innermost layers Hits in the innermost layers

are matched in r-are matched in r- and r-z and r-z– Pixel seeds formed if Pixel seeds formed if

transverse i.p. < 1mm and transverse i.p. < 1mm and within the luminous region within the luminous region in zin z

• PV findingPV finding– Clusters of tracks along the Clusters of tracks along the

beam axisbeam axis– PV candidate: largest PV candidate: largest

number of tracks with number of tracks with highest scalar phighest scalar pTT sum sum

• Using full Tracker Using full Tracker reconstructionreconstruction– Combinatorial algorithm Combinatorial algorithm 22 based rejection based rejection

Primary vertex Primary vertex reconstructionreconstruction

= 26 = 26 mm

•Pixel - Resolution in z (cm)

Using only the PixelsUsing only the Pixels: fast, resolution : fast, resolution ~ 30~ 30 m m in z (QCD in z (QCD events)events)

Using full TrackerUsing full Tracker: slower, better resolution : slower, better resolution ~15~15 m m in z in z (uu events)(uu events)

•Full Tracker- Resolution in z (cm)

Jet reconstructionJet reconstruction

Calorimetry data organized in Calorimetry data organized in towerstowers (HCAL (HCAL 0.087x 0.087x 0.087 barrel, 0.087 barrel, 0.175 x 0.175 x 0.175 end-caps, 0.175 end-caps,

25 crystal ECAL -> 1 HCAL 25 crystal ECAL -> 1 HCAL tower).tower).

Iterative cone algorithmIterative cone algorithm with with calo (ECAL+HCAL) tower as calo (ECAL+HCAL) tower as input.input.

Proto-jet is defined as Proto-jet is defined as

Et = Et = Et Etii , ,

= = ii Et Etii// Et Etii

= = ii Et Etii/ / Et Eti i

Iteration until Iteration until

|Et |Et n+1n+1 –Et –Et nn|<|<

Jet Cone and Jet Cone and Tracks SelectionTracks Selection

•Optimize cone Optimize cone sizesize

Impact Parameter Impact Parameter SignificanceSignificance

• 3 dim

•Simply tag jets by requiring a minimum number of tracks exceeding a given i.p. significance

• 2 dim

Simple impact Simple impact parameter Tag parameter Tag

Impact parameter Impact parameter Probability TagProbability Tag

•Originally developed by Originally developed by ALEPHALEPH

•Tracks with negative impact parameter d can be used to measure the intrinsic resolution

•Confidence level that a track with impact parameter significance S originates from the primary vertex :

T xxRSP d)()( Impact parameter significance

Impact parameter Impact parameter Probability TagProbability Tag

•The probability that a set of tracks is coming from the primary vertex can be computed as

•By constructionBy construction the track impact parameter C.L. for tracks coming from primary vertex is flat•If a track comes from a displaced vertex its C.L. is very small

itrack 1

Track confidence level

Impact parameter Impact parameter Probability Tag Probability Tag

Divide tracks into classes Divide tracks into classes

depending on p and depending on p and

Confidence levelsConfidence levels

2 dim 3 dim

100 GeV100 GeV

BarrelBarrel

Probability tag Probability tag PerformancePerformance

Secondary vertex Secondary vertex based tagsbased tags

Fast Reconstruction Linearise tracks around the

origin (valid if secondary vertex not too far and if pT is sufficiently large)

For each track measure the transverse impact parameter d0

and its azimutal angle which are related with the vertex position (l,B)

Each track coming from the same secondary vertex has the same l and B

d0 = l sin(-B) l (-B)

Sec. Vtx

Origin Primary vertexx

The dThe d00-- plane plane

• Tracks coming from the same secondary vertex– have relatively large d0

– are aligned on a positive slope segment

• Tracks from origin lie around d0~0 and at any angle

In the d0-plane a track is a point

B tracks P.V. tracks

Positive slope

d0 = l -l B

A typical event

How to find seedsHow to find seeds

Links • Segment connecting 2 tracks

close in and • positive slope

Clusters • a 2-track cluster is a link• check if 2 links are close in

the d0-- space 3-tracks cluster

• Merge clusters with links in common many tracks clusters

• The vertex seeds are the clusters which remain at the end of the iteration

Good Links

Bad Link

Cluster

d0 = l -l B

BackgroundsBackgrounds

Interactions in the beam pipe

Radial distance (cm)

BackgroundsBackgrounds

Number of tracks in the vertex(Barrel region, ET=100 GeV)

•Tighten cuts on 2 tracks’ vertices:

Require positive impact parameter to tracks belonging to vertices

Secondary Vertex Secondary Vertex tags Performancetags Performance

Decay length significance(before all other cuts applied)

•Simple selection based on decay length decay length significance in 3-dimsignificance in 3-dim

Secondary Vertex Secondary Vertex Tags PerformanceTags Performance

Tracks’ TuningsTracks’ Tunings

Track Track counting counting

algorithm algorithm

•Optimize this

Probability Probability Tag Tag

algorithm algorithm

•Optimize maximum track

probability

Secondary Secondary Vertex Tag Vertex Tag algorithm algorithm

•Optimize track impact parameter

Comparisons Comparisons between between

algorithmsalgorithms

algorithms algorithms

Charm jetsCharm jets

algorithms - algorithms - charmcharm

Tag correlationsTag correlations

Impact parameter significanceSecon

ifican

High Level High Level TriggersTriggers

• No b primitives at L1• Start from L1 or L2 jets in the

calorimeters• Aim to reduce the rate using b-tag

at HLT

Conditional Track Conditional Track ReconstructionReconstruction

Recipe for B Recipe for B inclusive triggersinclusive triggers

1. From pixel hits and calorimeters:– The seed for tracks reconstruction is

created around the LVL1 jet direction– Primary vertex is calculated

2. Tracks are reconstructed in a cone of R<0.4 around the jet direction

3. Tracks are conditionally reconstructed4. Refine the jet direction by using the

reconstructed tracks

L1+Tracks B-tagL1+Tracks B-tagEt=100 GeV jets

barrel 0.<|η|<0.7Online

performance is better with L1+Tk jets!!

OFFLINE

Jet-tag: 2 tracks with SIP>0.5,1.,1.5,2.,2.5,3.,3.5,4.

Jet reconstructionJet reconstruction

L1 jets η

L2 jets η

L1 jets + Tk η

L1 jets φ

L2 jets φ

L1 jets + Tk φ

ση=0.112

ση~0.037

ση~0.025

•Raw Calo Level 1 Raw Calo Level 1

•Calorimeter Level 2 jets Calorimeter Level 2 jets

•Calorimeter Level 2 + TracksCalorimeter Level 2 + Tracks

Sign flip of IPSign flip of IP

L1 jet (poor) resolution in η and φ (σ~0.1)

2d transverse IP sign flip

ηrec- ηsim

ση~0.1u

b OFFLINE – Lucell

HLT-L1 Jets

L1+Tracks B-tag L1+Tracks B-tag (2)(2)

Et=100 GeV jets

barrel 0.<|η|<0.7Better b jets

efficiency with 3d IP

Jet-tag: 2 tracks with SIP>0.5,1.,1.5,2.,2.5,3.,3.5,4.

OFFLINE

Timing for b jetsTiming for b jets

0- 0,7 1,2- 1,6 1,6- 2,0 2,0- 2,4

Tagging

Reconstruction

Pixel Readout

Expect to gain at least factor 2

Timing for u jetsTiming for u jets

0- 0,7 0,7- 1,2 1,2- 1,6 1,6- 2,0 2,0- 2,4

Tagging

Reconstruction

Pixel Readout

Expect to gain at least factor 2

Efficiency for b Efficiency for b jetsjets

0-0,7 1,2-1,6 1,6-2,0 2,0-2,4

0.0000

0.0020

0.0040

0.0060

0.0080

0.0100

0.0120

Effi ciency Effi ciency for b tracks Tag Effi ciency Fake Rate

Efficiency for u Efficiency for u jetsjets

0- 0,7 0,7- 1,2 1,2- 1,6 1,6- 2,0 2,0- 2,4

0.0000

0.0010

0.0020

0.0030

0.0040

0.0050

0.0060

Efficiency Tag Efficiency Fake Rate

B-tag B-tag performanceperformance

offline

Impact Parameter Significance Tag (not optimised)

Inclusive Jet RateInclusive Jet Rate

Inclusive HLT jet rate

pt= 50÷170 GeV

2.4 KHz @ 120 GeV

Number of B’s Number of B’s and C’s in the and C’s in the central regioncentral region

Fraction of events with at least 1 b or c jet:ƒb>0~6%

ƒc>0~11% with at least 2 b or c jets:

ƒb>1~1.6%

ƒc>1~2.4%

Inclusive jet Rate Inclusive jet Rate and tagand tag

2 jets inside Tracker

Ejet>25 GeV

Tag: 2x3Tag: 2x3

A tour in B physics … A tour in B physics …

BJ/ reconstruction

pt<2 GeV @ 5σ

hit=5 or σ(pt)/pt<0.02

max n. of cand=2

Overall efficiency ~11%Background rate: from 16 to 0.4 Hz

J/J/ mass mass resolutionresolution

Partial reco Full reco

Vertex recoVertex reco

Full Partial

x vertex resolution (m)

Conclusions Conclusions

• Several robust tags available with good performances– More tags still in implementation (leptons …)

• HLT looks promising– Detailed investigations for performance at high

luminosity

ConclusionsConclusions

•Helsinki temperatureIn a few time btag activities will rise in as Helsinki temperature!

B Tagging with CMS Fabrizio Palla INFN Pisa B Workshop Helsinki 29 May – 1 June 2002

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