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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
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
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
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
IntroductionIntroduction
•Example:Example:Effects on h bb decay reconstruction in MSUGRA
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
The problem definition The problem definition How a “real” 2-
jet event looks like:
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
Tags IngredientsTags Ingredients
1. Track reconstruction2. Transverse and longitudinal impact
parameter 3. Primary vertex reconstruction in z4. Jet reconstruction5. Vertex reconstruction
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
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
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
Track Track ReconstructionReconstruction
<10-
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
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
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
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
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)
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
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|<|<
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
Jet Cone and Jet Cone and Tracks SelectionTracks Selection
bb
uu
•Optimize cone Optimize cone sizesize
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
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
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
Simple impact Simple impact parameter Tag parameter Tag
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
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
d
dSSR
:)(
•Confidence level that a track with impact parameter significance S originates from the primary vertex :
S
T xxRSP d)()( Impact parameter significance
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
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
)(
;!
ln
itrack 1
1
0
TNi
N
j
j
PP
j
PP
Track confidence level
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
Impact parameter Impact parameter Probability Tag Probability Tag
Divide tracks into classes Divide tracks into classes
depending on p and depending on p and
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
Confidence levelsConfidence levels
2 dim 3 dim
100 GeV100 GeV
BarrelBarrel
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
Probability tag Probability tag PerformancePerformance
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
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)
d0 B
l
Track
Sec. Vtx
Origin Primary vertexx
y
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
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
d0
B tracks P.V. tracks
Positive slope
d0 = l -l B
A typical event
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
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
d0
Bad Link
Cluster
d0 = l -l B
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
BackgroundsBackgrounds
Interactions in the beam pipe
Radial distance (cm)
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
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
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
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
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
Secondary Vertex Secondary Vertex Tags PerformanceTags Performance
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
Tracks’ TuningsTracks’ Tunings
Track Track counting counting
algorithm algorithm
•Optimize this
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
Tracks’ TuningsTracks’ Tunings
Probability Probability Tag Tag
algorithm algorithm
•Optimize maximum track
probability
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
Tracks’ TuningsTracks’ Tunings
Secondary Secondary Vertex Tag Vertex Tag algorithm algorithm
•Optimize track impact parameter
sign
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
Comparisons Comparisons between between
algorithmsalgorithms
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
Comparisons Comparisons between between
algorithms algorithms
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
Charm jetsCharm jets
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
Comparisons Comparisons between between
algorithms - algorithms - charmcharm
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
Comparisons Comparisons between between
algorithms - algorithms - charmcharm
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
Tag correlationsTag correlations
Impact parameter significanceSecon
dary
vert
ex s
ign
ifican
ce
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
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
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
Conditional Track Conditional Track ReconstructionReconstruction
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
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
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
L1+Tracks B-tagL1+Tracks B-tagEt=100 GeV jets
barrel 0.<|η|<0.7Online
performance is better with L1+Tk jets!!
OFFLINE
HLT
Jet-tag: 2 tracks with SIP>0.5,1.,1.5,2.,2.5,3.,3.5,4.
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
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
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
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
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
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
HLT
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
Timing for b jetsTiming for b jets
0.000
0.100
0.200
0.300
0.400
0.500
0- 0,7 1,2- 1,6 1,6- 2,0 2,0- 2,4
Range
1GH
z CPU
s/e
v.
Tagging
Reconstruction
Pixel Readout
Expect to gain at least factor 2
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
Timing for u jetsTiming for u jets
0.000
0.050
0.100
0.150
0.200
0.250
0.300
0.350
0.400
0- 0,7 0,7- 1,2 1,2- 1,6 1,6- 2,0 2,0- 2,4
Range
1GH
z CPU
s/e
v.
Tagging
Reconstruction
Pixel Readout
Expect to gain at least factor 2
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
Efficiency for b Efficiency for b jetsjets
0.000
0.200
0.400
0.600
0.800
1.000
0-0,7 1,2-1,6 1,6-2,0 2,0-2,4
Range
Effi
cien
cy
0.0000
0.0020
0.0040
0.0060
0.0080
0.0100
0.0120
Fak
e Rat
e
Effi ciency Effi ciency for b tracks Tag Effi ciency Fake Rate
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
Efficiency for u Efficiency for u jetsjets
0.000
0.200
0.400
0.600
0.800
1.000
0- 0,7 0,7- 1,2 1,2- 1,6 1,6- 2,0 2,0- 2,4
Range
Effi
cien
cy
0.0000
0.0010
0.0020
0.0030
0.0040
0.0050
0.0060
Fak
e Rat
e
Efficiency Tag Efficiency Fake Rate
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
B-tag B-tag performanceperformance
offline
HLT
offline
HLT
Impact Parameter Significance Tag (not optimised)
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
Inclusive Jet RateInclusive Jet Rate
Inclusive HLT jet rate
pt= 50÷170 GeV
2.4 KHz @ 120 GeV
^
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
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%
Allc
b
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
Inclusive jet Rate Inclusive jet Rate and tagand tag
2 jets inside Tracker
Ejet>25 GeV
Tag: 2x3Tag: 2x3
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
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
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
J/J/ mass mass resolutionresolution
Partial reco Full reco
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
Vertex recoVertex reco
Full Partial
x vertex resolution (m)
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
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
B WorkshopHelsinki 2002
b Tagging with CMS Fabrizio PallaINFN Pisa
ConclusionsConclusions
•Helsinki temperatureIn a few time btag activities will rise in as Helsinki temperature!