introduction to b-tagging - ucl hep group · » light-flavour and c-jet rejection as a function of...
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Introduction to b-Tagging
Andrew Bell
HEP Postgraduate Lecture Course 21.11.17
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Motivation
» Many interesting physics signatures produce a final-state b-quark: » Top decays (|Vtb| >> |Vts|), Higgs→bb, BSM physics searches,
precision standard model measurements etc.
» Final state b-quark will hadronise and shower » Reconstructed as a jet » What properties can we use to differentiate these jets from
others?
» Truth flavour label assigned in simulation using ΔR matching between hadron and jet:
» If b-hadron present, label as “b-jet” » Else if no b-hadron but c-hadron present, label as “c-jet” (c-hadrons have
similar properties to b-hadrons) » Else label as a “light-flavour jet”
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b-Hadron Properties» Final state b-quark fragments (production of hadrons from quarks) to excited b-hadrons:
» B*/B** ~87% of the time » These decay strongly or electromagnetically into a stable b-hadron (with a few additional
particles) (left figure) » b-quark fragmentation is quite hard:
» ~70% of b-quark energy goes to b-hadron (right figure) » b-hadrons typically decay to produce ~5 charged stable decay products
» c-hadrons typically decay to produce ~2 charged stable decay products
Weakly decaying B hadrons 0B +B 0
sB +cB 0
bΛ -bΞ 0
bΞ -bΩ -
bΣ 0bΣ +
bΣ
Prod
uctio
n fra
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Pythia8
PYTHIA6
Herwig++
HERWIG
ATLAS Simulation Preliminary
2|jet
/|pC
p⋅ jet
p≡z 0 0.2 0.4 0.6 0.8 1
1/N
dN
/dz
0
0.05
0.1
0.15
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0.25ATLAS Simulation Preliminary Pythia8
PYTHIA6Herwig++HERWIG
t t→POWHEG pp R=0.4 b-jetstAnti-k
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[mm]τ c+B0.46 0.48 0.5 0.52 0.54 0.56 0.58 0.6 0.62 0.64
0.0005 ) ±PYTHIA8 (0.4904 0.0005 ) ±Pythia6 (0.4620
0.0005 ) ±HERWIG (0.4942 0.0005 ) ±Herwig++ (0.5016
0.0004 ) ±EvtGen 2.0 (0.4919 0.0024)±PDG (0.4923
EvtGen (0.4920)
ATLAS Simulation Preliminary
4
b-Hadron Lifetimes» Lifetime of b-hadron is typically ~1.5 ps » Plots below compare decay distances of b-hadrons in a number of commonly used MC
generators » EvtGen is used throughout ATLAS as MC “afterburner”:
» Provide updated lifetime and decay information » More realistic modelling and improve the description of the particle decay modes
and multiplicities » Equivalent plots for c-hadrons in back-up
[mm]τ c0B0.4 0.45 0.5 0.55 0.6 0.65 0.7
0.0005 ) ±PYTHIA8 (0.4591 0.0005 ) ±Pythia6 (0.4676
0.0005 ) ±HERWIG (0.4829 0.0005 ) ±Herwig++ (0.4608
0.0003 ) ±EvtGen 2.0 (0.4580 0.0021)±PDG (0.4557
EvtGen (0.4555)
ATLAS Simulation Preliminary
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semileptonic fraction+B0.1 0.12 0.14 0.16 0.18 0.2 0.22 0.24 0.26
0.0003 ) ±PYTHIA8 (0.1122 0.0003 ) ±Pythia6 (0.1054
0.0003 ) ±HERWIG (0.0956 0.0004 ) ±Herwig++ (0.1088
0.0008 ) ±EvtGen 2.0 (0.0968 0.0002 ) ±EvtGen ATLAS .dec (0.1108
0.0028)±PDG (0.1099 EvtGen (0.0980)
ATLAS Simulation Preliminary
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b-Hadron Lifetimes» b-hadrons decay semileptonically ~10% of the time (i.e. produces an electron or muon) » Plots below compare semileptonic fractions of b-hadrons in a number of commonly
used MC generators » EvtGen is used throughout ATLAS as MC “afterburner”:
» Provide updated lifetime and decay information » More realistic modelling and improve the description of the particle decay modes
and multiplicities » Equivalent plots for c-hadrons in back-up
semileptonic fraction0B0.09 0.1 0.11 0.12 0.13 0.14 0.15 0.16 0.17
0.0003 ) ±PYTHIA8 (0.1045 0.0003 ) ±Pythia6 (0.1050
0.0003 ) ±HERWIG (0.0962 0.0003 ) ±Herwig++ (0.0999
0.0007 ) ±EvtGen 2.0 (0.0934 0.0002 ) ±EvtGen ATLAS .dec (0.1039
0.0028)±PDG (0.1033 EvtGen (0.0949)
ATLAS Simulation Preliminary
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Jet axis
Primary vertex
Decay length Lxy
Track impact parameter
Secondary vertex
Tertiary vertex
6
Jet Properties» b-hadrons have a number of useful properties:
» Longer lifetimes (~1.5 ps) » b-hadron decays to a c-hadron (|Vcb| >> |Vub|) » Larger mass (~5 GeV) » High decay product multiplicity
» 3 “baseline” algorithms designed to target these properties
» Reconstruction of tracks is essential to b-tagging » Associated to jet using ΔR matching
» For a 30 GeV b-hadron » βɣc𝛕 ~ 5 mm - measurable!
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Light-flavour Jet Properties
» In a light-flavour jet, most tracks come directly from quark fragmentation » Can sometimes result in a displaced vertex and look like a b-jet:
» Interactions with the detector material » Photon conversions » Long-lived particles (Ks/Λ) » Badly measured tracks
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Track signed z0 significance (Good)20− 10− 0 10 20 30 40
Arbi
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uni
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c jetsLight-flavour jets
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Impact Parameter Algorithms» Impact parameters (IP) defined as signed point of closest approach in
longitudinal and transverse plane » IP Significance is the IP divided by its uncertainty » IP is signed: positive if crosses jet axis in front of primary vertex
(otherwise negative) » Construct reference templates (PDFs) for b-, c- and light-flavoured jets » Log likelihood ratio of probability for each jet flavour gives best
separation (Neyman-Pearson lemma) » Assume each track is uncorrelated
» IP2D uses just transverse (d0) IP (less susceptible to pileup) » IP3D uses transverse (d0) and longitudinal (z0) IP
Track signed d0 significance (Good)20− 10− 0 10 20 30 40
Arbi
trary
uni
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Impact Parameter Algorithms» Impact parameters (IP) defined as signed point of closest approach in
longitudinal and transverse plane » IP Significance is the IP divided by its uncertainty » IP is signed: positive if crosses jet axis in front of primary vertex
(otherwise negative) » Construct reference templates (PDFs) for b-, c- and light-flavoured jets » Log likelihood ratio of probability for each jet flavour gives best
separation (Neyman-Pearson lemma) » Assume each track is uncorrelated
» IP2D uses just transverse (d0) IP (less susceptible to pileup) » IP3D uses transverse (d0) and longitudinal (z0) IP
Product over all associated tracks
PDF for track of flavour j, with IPm
Probability for jet to have flavour j
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)u/PbIP3D log(P20− 10− 0 10 20 30 40 50
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Impact Parameter Algorithms
)u/PbIP2D log(P20− 10− 0 10 20 30 40 50
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ATLAS Simulation Preliminaryt = 13 TeV, ts b jets
c jetsLight-flavour jets
» Impact parameters (IP) defined as signed point of closest approach in longitudinal and transverse plane
» Significance is the IP divided by its uncertainty » IP is signed: positive if crosses jet axis in front of primary vertex
(otherwise negative) » Construct reference templates (PDFs) for b-, c- and light-flavoured jets » Log likelihood ratio of probability for each jet flavour gives best
separation (Neyman-Pearson lemma) » Assume each track is uncorrelated
» IP2D uses just transverse (d0) IP (less susceptible to pileup) » IP3D uses transverse (d0) and longitudinal (z0) IP
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Secondary Vertex (SV) Algorithms» Secondary vertex
algorithms aim to reconstruct the secondary decay point using tracks
» Firstly, reconstruct all 2-track vertices
» Combine into one secondary vertex (removing tracks which are not consistent with the SV)
» Many useful properties: » Decay length
(significance) » Vertex Mass » Number of tracks
etc.
Jet axis
Primary vertex
Decay length Lxy
Track impact parameter
Secondary vertex
Tertiary vertex
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Secondary Vertex (SV) Algorithms» Secondary vertex
algorithms aim to reconstruct the secondary decay point using tracks
» Firstly, reconstruct all 2-track vertices
» Combine into one secondary vertex (removing tracks which are not consistent with the SV)
» Many useful properties: » Decay length
(significance) » Vertex Mass » Number of tracks
etc. (SV)TrkAtVtxN2 4 6 8 10 12 14
Arbi
trary
uni
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4−10
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m(SV) [GeV]0 1 2 3 4 5 6
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(SV) [mm]xyL0 10 20 30 40 50 60 70 80 90
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(SV)xyzLσ/xyzL
0 10 20 30 40 50 60 70 80 90 100
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c jetsLight-flavour jets
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Decay Chain Reconstruction Algorithms» |Vcb| >> |Vub| → often produce c-hadron from b-hadron
decay » Additional tertiary vertex » JetFitter algorithm reconstructs all decay vertices
(assuming they lie on the same flight path) » Can even reconstruct single track vertices
» Vertex mass, decay length significance, number of vertices with at least 2 tracks etc.
(JF)1-trk vertices
N0 1 2 3 4 5 6
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Combining Taggers: Multivariate Algorithms» Three baseline algorithms provide b-jet discrimination → provide a number of
weakly correlated variables » Combine output of three baseline algorithms using a Boosted Decision Tree (BDT)
» Total of 24 input variables » Algorithm known as MV2c » Train using t t events, while controlling the c-/light-flavour jet background
» MV2cXX means trained on a sample with XX% c-jets (100-XX% light-flavour jets)
» Exposes training to different amounts of each jet type » Need to balance light-flavour and c-jet performance
» Efficiency defined as:
» Used to evaluate performance of an algorithm at a set “Working Point” (WP) » Rejection is defined as 1/efficiency
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MV2c10 BDT Output1− 0.8− 0.6− 0.4− 0.2− 0 0.2 0.4 0.6 0.8 1
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MV2c20 − 2015 configMV2c20 − 2016 configMV2c10 − 2016 configMV2c00 − 2016 config
b-jet efficiency0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
ATLAS Simulation Preliminary
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Multivariate Algorithms» Light-flavour and c-jet rejection as a function of b-jet tagging efficiency for a
number of training configurations » Larger area under curve → increased performance » MV2c10 chosen to balance performance of jet flavours
» Current default throughout ATLAS
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MV2c10 BDT Output1− 0.8− 0.6− 0.4− 0.2− 0 0.2 0.4 0.6 0.8 1
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MV2c20 − 2015 configMV2c20 − 2016 configMV2c10 − 2016 configMV2c00 − 2016 config
b-jet efficiency0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
ATLAS Simulation Preliminary
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Multivariate Algorithms» Light-flavour and c-jet rejection as a function of b-jet tagging efficiency for a
number of training configurations » Larger area under curve → increased performance » MV2c10 chosen to balance performance of jet flavours
» Current default throughout ATLAS
c-je
t rej
ectio
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MV2c20 − 2015 configMV2c20 − 2016 configMV2c10 − 2016 configMV2c00 − 2016 config
b-jet efficiency0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
2016
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MV2c10 BDT Output1− 0.8− 0.6− 0.4− 0.2− 0 0.2 0.4 0.6 0.8 1
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MV2c20 − 2015 configMV2c20 − 2016 configMV2c10 − 2016 configMV2c00 − 2016 config
b-jet efficiency0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
ATLAS Simulation Preliminary
t=13 TeV, ts
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Multivariate Algorithms» Light-flavour and c-jet rejection as a function of b-jet tagging efficiency for a
number of training configurations » Larger area under curve → increased performance » MV2c10 chosen to balance performance of jet flavours
» Current default throughout ATLAS
c-je
t rej
ectio
n
10
210
MV2c20 − 2015 configMV2c20 − 2016 configMV2c10 − 2016 configMV2c00 − 2016 config
b-jet efficiency0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
2016
/201
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ATLAS Simulation Preliminary
t=13 TeV, ts
0.6
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14b-jet tagging efficiency0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Ligh
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-jet r
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410MV1IP3D+JetFitterIP3D+SV1JetFitterIP3DSV1JetProb
=7 TeVs|<2.5jetη>20 GeV, |jet
Tp
ATLASSimulation
Individual Tagger Performance
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Differential Performance» b-tagging efficiency is dependent on jet and event variables:
» Performance shown for 70% WP » Efficiency drops at low pT due to reduced IP resolution » Drops at high pT due to b-hadron decaying after first ID layer
(reduced IP resolution) and tracks becoming more collimated » At high |η|, more material interactions within the ID, reducing IP resolution
[GeV]T
Jet p100 200 300 400 500 600
Effic
ienc
y
-310
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-110
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bε
b jetsc jetsLight-flavour jets
ATLAS Simulation Preliminaryt=13 TeV, ts
|ηJet |0 0.5 1 1.5 2 2.5
Effic
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-310
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bε
b jetsc jetsLight-flavour jets
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Calibrations» Up to now, all performance studies/training are from simulation » How well do we know the performance in data?
» Need to correct for detector and modelling effects » Apply scale factors to correct performance in MC to data
» Applied to event weight (for each jet)
» Select data sample pure in one jet flavour » Measure efficiency in data for a chosen jet flavour (j)
» Efficiency in MC is known from truth information » Derive scale factor as a function of jet pT and |η| » Efficiency measurements conducted at a set working point
Efficiency from data
Efficiency from simulation
Scale factor
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t t Calibration
[GeV]T
Jet p
0 50 100 150 200 250 300
b-je
t e
ffic
ien
cy
0.2
0.4
0.6
0.8
1
1.2
Data
MC
-1 = 13 TeV, 36.1 fbs
ATLAS Work in Progress
= 70%bεMV2c10,
R=0.4 calorimeter jetstAnti-k
[GeV]T
Jet p
0 50 100 150 200 250 300
MC
bε /
data
bε
0.7
0.8
0.9
1
1.1
1.2
1.3
Total Uncertainty
Stat. Uncertainty
-1 = 13 TeV, 36.1 fbs
ATLAS Work in Progress
= 70%bεMV2c10,
R=0.4 calorimeter jetstAnti-k
» b-jets tagging efficiency measured in data using di-leptonic t t events » ~70% pure in b-jets, almost no c-jet contamination
» Extract b-jet efficiency from fit to data » Dominated by systematic uncertainties from modelling of t t background:
» Systematics arising from b-tagging are a key factor in a many analyses
» ~2-5% uncertainties over majority of spectrum » Largest experimental systematic uncertainty in VH(→bb) analysis
» Consistent with unity for b-jets, but not always the case! » Light-flavour jets can have SF ~ 2
Source of uncertainty µ
Total 0.39
Statistical 0.24
Systematic 0.31
Experimental uncertainties
Jets 0.03
EmissT 0.03
Leptons 0.01
b-taggingb-jets 0.09
c-jets 0.04
light jets 0.04
extrapolation 0.01
Pile-up 0.01
Luminosity 0.04
Theoretical and modelling uncertainties
Signal 0.17
Floating normalisations 0.07
Z + jets 0.07
W + jets 0.07
tt 0.07
Single top quark 0.08
Diboson 0.02
Multijet 0.02
MC statistical 0.13
Source of uncertainty µ
Total 0.39
Statistical 0.24
Systematic 0.31
Experimental uncertainties
Jets 0.03
EmissT 0.03
Leptons 0.01
b-taggingb-jets 0.09
c-jets 0.04
light jets 0.04
extrapolation 0.01
Pile-up 0.01
Luminosity 0.04
Theoretical and modelling uncertainties
Signal 0.17
Floating normalisations 0.07
Z + jets 0.07
W + jets 0.07
tt 0.07
Single top quark 0.08
Diboson 0.02
Multijet 0.02
MC statistical 0.13
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Future Developments» Deep learning algorithms (DL1) » Recursive Neural Network (RNN)
» Takes into account track IP correlations to improve performance » Dedicated c-tagging algorithms (H→cc):
» Use DL algorithms or 2D cut on MV2c100 and MV2cl100 » More limited efficiency than b-tagging (typical WP ~ 25% c-jet efficiency)
» Inclusion of muon variables: » 10% of b-hadron decays produce a muon
bεb-jet efficiency, 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
lεlig
ht-je
t rej
ectio
n, 1
/
1
10
210
310 RNNIPIP3D
ATLAS Simulation Internalt=13 TeV, ts
|<2.5η>20 GeV, |T
p
Powered by TCPDF (www.tcpdf.org)Powered by TCPDF (www.tcpdf.org)Powered by TCPDF (www.tcpdf.org)Powered by TCPDF (www.tcpdf.org)Powered by TCPDF (www.tcpdf.org)
Preliminary
light
-jet r
ejec
tion
1
10
210
310
410
t=13 TeV, ts
MV2MV2MuMV2MuRnn
ATLAS Simulation Preliminary
b-jet efficiency
0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
ratio
to M
V2
0.60.8
11.21.41.61.8
2
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Useful links
» b-jet performance note (2017) - https://cds.cern.ch/record/2273281 » b-jet performance note (2016) - https://cds.cern.ch/record/2160731 » b-jet performance note (2015) - https://cds.cern.ch/record/2037697 » b-jet efficiency measurement (2012) - https://cds.cern.ch/record/1664335 » ATLAS Flavour Tagging Public Page - https://twiki.cern.ch/twiki/bin/view/
AtlasPublic/FlavourTaggingPublicResultsCollisionData » Comparison of MC generator predictions for b- and c- hadrons in the
decays of top quarks and the fragmentation of high pT jets: » https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-
PHYS-PUB-2014-008/
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Back-Up
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Tracking in ATLAS» Tracks used for b-tagging are reconstructed using the ATLAS inner detector (ID)
» Immersed in a 2 T solenoid magnetic field (essential for track momentum measurement) » Insertable B-Layer (new for Run-2) is the inner-most layer at ~30mm radius (silicon
pixels) » Greatly improved track resolution over Run-1
» Next layers (radially outwards): pixel layers, semiconductor tracker and transition radiation tracker
» Tracks of charged particles are reconstructed using hits in the ID » Associated to jets using a ΔR(track, jet) matching (jet pT dependant)
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Primary Vertex Reconstruction» Colliding bunches contain up to 10
11
protons » In 2016, up to 40 interactions per bunch crossing » Need to effectively find the primary vertex of the event of interest
» Reconstruct all vertices using ID tracks (example below has ~25 vertices) and an iterative procedure » Select vertex seed from beamspot and track information » Remove tracks from seed until passes set quality requirement » Use discarded tracks to seed another vertex, and repeat until all tracks associated to a vertex
» Primary vertex is chosen as the one with highest sum of squared track pT » Correctly identifies the primary vertex ~99% of the time
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b-Hadron Lifetimes
[mm]τ c0B0.4 0.45 0.5 0.55 0.6 0.65 0.7
0.0005 ) ±PYTHIA8 (0.4591 0.0005 ) ±Pythia6 (0.4676
0.0005 ) ±HERWIG (0.4829 0.0005 ) ±Herwig++ (0.4608
0.0003 ) ±EvtGen 2.0 (0.4580 0.0021)±PDG (0.4557
EvtGen (0.4555)
ATLAS Simulation Preliminary
[mm]τ c+B0.46 0.48 0.5 0.52 0.54 0.56 0.58 0.6 0.62 0.64
0.0005 ) ±PYTHIA8 (0.4904 0.0005 ) ±Pythia6 (0.4620
0.0005 ) ±HERWIG (0.4942 0.0005 ) ±Herwig++ (0.5016
0.0004 ) ±EvtGen 2.0 (0.4919 0.0024)±PDG (0.4923
EvtGen (0.4920)
ATLAS Simulation Preliminary
[mm]τ c0sB
0.42 0.44 0.46 0.48 0.5 0.52 0.54 0.56 0.58 0.6 0.62
0.0010 ) ±PYTHIA8 (0.4369 0.0012 ) ±Pythia6 (0.4820
0.0010 ) ±HERWIG (0.4595 0.0010 ) ±Herwig++ (0.4427
0.0007 ) ±EvtGen 2.0 (0.4395 0.0045)±PDG (0.4491
EvtGen (0.4415)
ATLAS Simulation Preliminary
[mm]τ cbΛ0.3 0.4 0.5 0.6 0.7 0.8
0.0013 ) ±PYTHIA8 (0.3682 0.0010 ) ±Pythia6 (0.3420
0.0008 ) ±HERWIG (0.2818 0.0009 ) ±Herwig++ (0.3686
0.0010 ) ±EvtGen 2.0 (0.3968 0.0096)±PDG (0.4275
EvtGen (0.4272)
ATLAS Simulation Preliminary
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b-Hadron Semileptonic Fractions
semileptonic fraction0B0.09 0.1 0.11 0.12 0.13 0.14 0.15 0.16 0.17
0.0003 ) ±PYTHIA8 (0.1045 0.0003 ) ±Pythia6 (0.1050
0.0003 ) ±HERWIG (0.0962 0.0003 ) ±Herwig++ (0.0999
0.0007 ) ±EvtGen 2.0 (0.0934 0.0002 ) ±EvtGen ATLAS .dec (0.1039
0.0028)±PDG (0.1033 EvtGen (0.0949)
ATLAS Simulation Preliminary
semileptonic fraction+B0.1 0.12 0.14 0.16 0.18 0.2 0.22 0.24 0.26
0.0003 ) ±PYTHIA8 (0.1122 0.0003 ) ±Pythia6 (0.1054
0.0003 ) ±HERWIG (0.0956 0.0004 ) ±Herwig++ (0.1088
0.0008 ) ±EvtGen 2.0 (0.0968 0.0002 ) ±EvtGen ATLAS .dec (0.1108
0.0028)±PDG (0.1099 EvtGen (0.0980)
ATLAS Simulation Preliminary
semileptonic fraction0sB
0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45
0.0007 ) ±PYTHIA8 (0.0934 0.0008 ) ±Pythia6 (0.1062
0.0007 ) ±HERWIG (0.0969 0.0007 ) ±Herwig++ (0.0939
0.0017 ) ±EvtGen 2.0 (0.0928 0.0005 ) ±EvtGen ATLAS .dec (0.0928
0.0270)±PDG (0.0950 EvtGen (0.0930)
ATLAS Simulation Preliminary
semileptonic fraction0bΛ
0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45
0.0009 ) ±PYTHIA8 (0.0763 0.0009 ) ±Pythia6 (0.1059
0.0008 ) ±HERWIG (0.0973 0.0008 ) ±Herwig++ (0.0978
0.0027 ) ±EvtGen 2.0 (0.1207 0.0008 ) ±EvtGen ATLAS .dec (0.1003
0.0230)±PDG (0.0980 EvtGen (0.1233)
ATLAS Simulation Preliminary
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c-Hadron Lifetimes
[mm]τ c0D0.12 0.125 0.13 0.135 0.14 0.145
0.0001 ) ±PYTHIA8 (0.1230 0.0001 ) ±Pythia6 (0.1246
0.0002 ) ±HERWIG (0.1238 0.0001 ) ±Herwig++ (0.1231
0.0001 ) ±EvtGen 2.0 (0.1229 0.0004)±PDG (0.1230
EvtGen (0.1230)
ATLAS Simulation Preliminary
[mm]τ c+D0.3 0.32 0.34 0.36 0.38 0.4 0.42 0.44
0.0005 ) ±PYTHIA8 (0.3115 0.0005 ) ±Pythia6 (0.3161
0.0006 ) ±HERWIG (0.3150 0.0005 ) ±Herwig++ (0.3116
0.0003 ) ±EvtGen 2.0 (0.3114 0.0021)±PDG (0.3120
EvtGen (0.3118)
ATLAS Simulation Preliminary
[mm]τ c+sD
0.13 0.14 0.15 0.16 0.17 0.18 0.19 0.2
0.0004 ) ±PYTHIA8 (0.1498 0.0004 ) ±Pythia6 (0.1400
0.0004 ) ±HERWIG (0.1402 0.0005 ) ±Herwig++ (0.1494
0.0003 ) ±EvtGen 2.0 (0.1499 0.0021)±PDG (0.1500
EvtGen (0.1499)
ATLAS Simulation Preliminary
[mm]τ c+cΛ
0 0.05 0.1 0.15 0.2 0.25
0.0002 ) ±PYTHIA8 (0.0597 0.0002 ) ±Pythia6 (0.0621
0.0002 ) ±HERWIG (0.0614 0.0002 ) ±Herwig++ (0.0595
0.0002 ) ±EvtGen 2.0 (0.0596 0.0018)±PDG (0.0600
EvtGen (0.0598)
ATLAS Simulation Preliminary
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b-Hadron Semileptonic fractions
semileptonic fraction0D0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 0.22 0.24
0.0003 ) ±PYTHIA8 (0.0680 0.0003 ) ±Pythia6 (0.0767
0.0004 ) ±HERWIG (0.0810 0.0003 ) ±Herwig++ (0.0671
0.0003 ) ±EvtGen 2.0 (0.1072 0.0002 ) ±EvtGen ATLAS .dec (0.0678
0.0011)±PDG (0.0649 EvtGen (0.1071)
ATLAS Simulation Preliminary
semileptonic fraction+D0.1 0.2 0.3 0.4 0.5 0.6 0.7
0.0006 ) ±PYTHIA8 (0.1697 0.0007 ) ±Pythia6 (0.1725
0.0007 ) ±HERWIG (0.1260 0.0007 ) ±Herwig++ (0.1606
0.0007 ) ±EvtGen 2.0 (0.2009 0.0004 ) ±EvtGen ATLAS .dec (0.1697
0.0030)±PDG (0.1607 EvtGen (0.2009)
ATLAS Simulation Preliminary
semileptonic fraction+sD
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35
0.0007 ) ±PYTHIA8 (0.0695 0.0009 ) ±Pythia6 (0.0809
0.0007 ) ±HERWIG (0.0533 0.0009 ) ±Herwig++ (0.0699
0.0007 ) ±EvtGen 2.0 (0.0703 0.0005 ) ±EvtGen ATLAS .dec (0.0696
0.0040)±PDG (0.0650 EvtGen (0.0698)
ATLAS Simulation Preliminary
semileptonic fraction+cΛ
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2
0.0009 ) ±PYTHIA8 (0.0447 0.0008 ) ±Pythia6 (0.0448
0.0005 ) ±HERWIG (0.0372 0.0007 ) ±Herwig++ (0.0449
0.0008 ) ±EvtGen 2.0 (0.0466 0.0006 ) ±EvtGen ATLAS .dec (0.0471
0.0170)±PDG (0.0450 EvtGen (0.0480)
ATLAS Simulation Preliminary