Introduction to b-Tagging - UCL HEP Group · » Light-flavour and c-jet rejection as a function of b-jet tagging efficiency for a number of training configurations » Larger area

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)


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)


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)


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


0.0028)±PDG (0.1033 EvtGen (0.0949)


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



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




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



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



» 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




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


m(SV) [GeV]0 1 2 3 4 5 6

Arbi

trary

uni

ts

3−10

2−10

1−10

1

10



(SV) [mm]xyL0 10 20 30 40 50 60 70 80 90

Arbi

trary

uni

ts

3−10

2−10

1−10

1

10



(SV)xyzLσ/xyzL

0 10 20 30 40 50 60 70 80 90 100

Arbi

trary

uni

ts

2−10

1−10

1

10



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


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



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



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


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



light

-jet r

ejec

tion

10

210

310

410

510


b-jet efficiency0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1


t=13 TeV, ts

2016

/201

5 co

nfig

0.61

1.41.82.22.6

13




c-je

t rej

ectio

n

10

210


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


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



light

-jet r

ejec

tion

10

210

310

410

510


b-jet efficiency0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1


t=13 TeV, ts

2016

/201

5 co

nfig

0.61

1.41.82.22.6

13




c-je

t rej

ectio

n

10

210


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


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


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


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


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/

https://cds.cern.ch/record/2273281




https://twiki.cern.ch/twiki/bin/view/AtlasPublic/FlavourTaggingPublicResultsCollisionData

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)


[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)


[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)


[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)


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


0.0028)±PDG (0.1033 EvtGen (0.0949)


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


0.0028)±PDG (0.1099 EvtGen (0.0980)


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


0.0270)±PDG (0.0950 EvtGen (0.0930)


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


0.0230)±PDG (0.0980 EvtGen (0.1233)


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)


[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)


[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)


[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)


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


0.0011)±PDG (0.0649 EvtGen (0.1071)


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


0.0030)±PDG (0.1607 EvtGen (0.2009)


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


0.0040)±PDG (0.0650 EvtGen (0.0698)


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


0.0170)±PDG (0.0450 EvtGen (0.0480)


Documents

Introduction to b-Tagging - UCL HEP Group · » Light-flavour and c-jet rejection as a function of b-jet tagging efficiency for a number of training configurations » Larger area