Introduction to b-Tagging - UCL HEP Group · » Light-flavour and c-jet rejection as a function of...
Transcript of Introduction to b-Tagging - UCL HEP Group · » Light-flavour and c-jet rejection as a function of...
Introduction to b-Tagging
Andrew Bell
HEP Postgraduate Lecture Course 21.11.17
2
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”
3
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
ctio
n
0
0.1
0.2
0.3
0.4
0.5
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
0.2
0.25ATLAS Simulation Preliminary Pythia8
PYTHIA6Herwig++HERWIG
t t→POWHEG pp R=0.4 b-jetstAnti-k
[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
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
5
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
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!
7
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
Track signed z0 significance (Good)20− 10− 0 10 20 30 40
Arbi
trary
uni
ts
6−10
5−10
4−10
3−10
2−10
1−10
1
10
ATLAS Simulation Preliminaryt = 13 TeV, ts b jets
c jetsLight-flavour jets
8
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
ts
6−10
5−10
4−10
3−10
2−10
1−10
1
10
ATLAS Simulation Preliminaryt = 13 TeV, ts b jets
c jetsLight-flavour jets
8
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
)u/PbIP3D log(P20− 10− 0 10 20 30 40 50
Arbi
trary
uni
ts
6−10
5−10
4−10
3−10
2−10
1−10
1
10
ATLAS Simulation Preliminaryt = 13 TeV, ts b jets
c jetsLight-flavour jets
9
Impact Parameter Algorithms
)u/PbIP2D log(P20− 10− 0 10 20 30 40 50
Arbi
trary
uni
ts
6−10
5−10
4−10
3−10
2−10
1−10
1
10
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
10
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
10
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
ts
5−10
4−10
3−10
2−10
1−10
1
10 ATLAS Simulation Preliminaryt = 13 TeV, ts b jets
c jetsLight-flavour jets
m(SV) [GeV]0 1 2 3 4 5 6
Arbi
trary
uni
ts
3−10
2−10
1−10
1
10
ATLAS Simulation Preliminaryt = 13 TeV, ts b jets
c jetsLight-flavour jets
(SV) [mm]xyL0 10 20 30 40 50 60 70 80 90
Arbi
trary
uni
ts
3−10
2−10
1−10
1
10
ATLAS Simulation Preliminaryt = 13 TeV, ts b jets
c jetsLight-flavour jets
(SV)xyzLσ/xyzL
0 10 20 30 40 50 60 70 80 90 100
Arbi
trary
uni
ts
2−10
1−10
1
10
ATLAS Simulation Preliminaryt = 13 TeV, ts b jets
c jetsLight-flavour jets
11
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
Arbi
trary
uni
ts
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
b jetsc jetsLight-flavour jets
ATLAS Simulation Preliminary
t=13 TeV, ts
(JF)TrkAtVtx
N0 1 2 3 4 5 6 7 8
Arbi
trary
uni
ts
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
b jetsc jetsLight-flavour jets
ATLAS Simulation Preliminary
t=13 TeV, ts
12
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
MV2c10 BDT Output1− 0.8− 0.6− 0.4− 0.2− 0 0.2 0.4 0.6 0.8 1
Arbi
trary
uni
ts
3−10
2−10
1−10
1
10
ATLAS Simulation Preliminaryt = 13 TeV, ts b jets
c jetsLight-flavour jets
light
-jet r
ejec
tion
10
210
310
410
510
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
2016
/201
5 co
nfig
0.61
1.41.82.22.6
13
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
MV2c10 BDT Output1− 0.8− 0.6− 0.4− 0.2− 0 0.2 0.4 0.6 0.8 1
Arbi
trary
uni
ts
3−10
2−10
1−10
1
10
ATLAS Simulation Preliminaryt = 13 TeV, ts b jets
c jetsLight-flavour jets
light
-jet r
ejec
tion
10
210
310
410
510
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
2016
/201
5 co
nfig
0.61
1.41.82.22.6
13
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
5 co
nfig
ATLAS Simulation Preliminary
t=13 TeV, ts
0.6
1
1.4
1.8
MV2c10 BDT Output1− 0.8− 0.6− 0.4− 0.2− 0 0.2 0.4 0.6 0.8 1
Arbi
trary
uni
ts
3−10
2−10
1−10
1
10
ATLAS Simulation Preliminaryt = 13 TeV, ts b jets
c jetsLight-flavour jets
light
-jet r
ejec
tion
10
210
310
410
510
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
2016
/201
5 co
nfig
0.61
1.41.82.22.6
13
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
5 co
nfig
ATLAS Simulation Preliminary
t=13 TeV, ts
0.6
1
1.4
1.8
14b-jet tagging efficiency0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Ligh
t-fla
vour
-jet r
ejec
tion
1
10
210
310
410MV1IP3D+JetFitterIP3D+SV1JetFitterIP3DSV1JetProb
=7 TeVs|<2.5jetη>20 GeV, |jet
Tp
ATLASSimulation
Individual Tagger Performance
15
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
-210
-110
1
MV2c20=70%
bε
b jetsc jetsLight-flavour jets
ATLAS Simulation Preliminaryt=13 TeV, ts
|ηJet |0 0.5 1 1.5 2 2.5
Effic
ienc
y
-310
-210
-110
1
MV2c20=70%
bε
b jetsc jetsLight-flavour jets
ATLAS Simulation Preliminaryt=13 TeV, ts
16
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
17
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
18
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
19
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/
20
Back-Up
21
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)
22
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
23
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
24
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
25
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
26
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