研究会「LHCが切り拓く新しい素粒子物理学」
東京大学，2008年3月28日

Jet, Missing ET,
andEnergy Flow
寺師

弘二

東京大学素粒子物理国際研究センター
1Jet and MET❖ Jets and MissET are copiously producedat hadron colliders

Which particles
contribute tojet and MET?

- mini-jets, QCD jets
- tt → WbWb → lν + qq + bb
- SUSY → multi-jets + 2 LSPs
- Black Hole → multi-jets- .....

❖ We would like them to represent‣ Jet : collimated spray of particles(or quarks and gluons)‣ MissET: non-interacting particles
e.g, neutrinos, LSP in SUSY, ...

❖ Most challenging physics objects toreliably measure‣ Theoretically not unique (jet)‣ Detector and environmental limitations
2

Aim (or Outline) of This Talk
How do we measure jets and Missing ET at ATLAS?

‣ Hadronic energy measurement in calorimeters‣ Jet and Missing ET reconstruction‣ Expected performance‣ Performance validation with real data

How can we improve baseline performance?
➔ Energy flow approach (one possibility...)3

Hadronic Energy Measurement
Generic feature of hadronic shower in calorimeters‣ More complex than EM showers❖ Visible EM ~50% : e, γ, π0→γγ❖ Visible non-EM ~25% : h±, μ± ionization❖ Invisible ~25% : nuclear break-up and excitation❖ Escaped ~2% : ν, µ±‣ Only part of the deposited energy is sampled‣ Fractions are energy dependent‣ Large fluctuations
Hadronic energy measurement at ATLAS❖ Uses similar e/h for LAr and Tile and longitudinal samplings(→3-D signal clustering)❖ Provides some degree of software compensation❖ Accounts for invisible and escaped energy
4

ATLAS Calorimeters
Hadronic Endcap‣ Liquid Argon/Cu parallel plate structure‣ Δη x Δφ = 0.1 x 0.1 (1.5<|η|<2.5)‣ Δη x Δφ = 0.2 x 0.2 (2.5<|η|<3.2)‣ 4 samplings

Hadronic Barrel‣ Scintillator/Fe in tiled readout‣ Δη x Δφ = 0.1 x 0.1‣ Coverage |η|<1.7‣3 longitudinal samplings

Electromagnetic Barrel and Endcap‣ Liquid Argon/Pb accordion structure‣ Highly granular readout (~170,000 channels)‣ 0.0025 ≤ Δη ≤ 0.05, 0.025 ≤ Δφ ≤ 0.1‣ Coverage |η|<3.2, pre-sampler up to |η|<1.8‣2-3 longitudinal samplings

5

Forward‣ Liquid Argon/Cu or W absorbers‣ Non-projective geometry‣ Δη x Δφ ≈ 0.2 x 0.2 (3.2<|η|<4.9)‣ 3 samplings

Hadronic Signal in Calorimeters‣ LAr/Tile calorimeter readouts sample a shaped physics pulse from thecalorimeter in synch with 25 ns clock‣ For LAr:

❖ Signal reconstruction based on Optimal Filtering procedure (5 samples)
5

Energy = ADC2MeV ∙ ∑ ai (ADCi - pedestal)
i=1❖ Optimal Filtering Coefficients (OFC) ai calculated from:✓ Known physics pulse shape- Pulser system in case of EMB, EMEC and HEC- Beam tests (so far) for the FCAL

✓ Known pedestals and noise autocorrelation matrix- From noise measured in random trigger events❖ ADC2MeV = ADC2DAC x DAC2μA x μA2MeV(from calibration pulser system and beam tests)❖ Reconstructed energy is at EM scale (→ subject to hadronic calibration)
6

Noise in Calorimeters
Electronic Noise

Electronic noise‣ Unavoidable basic fluctuation on topof each calorimeter cell signal‣ Obtained from test beam andcommissioning phase‣ Ranges from ~10 MeV (central) to~600 MeV (forward) per cell

Pile-up noise‣ Depends on instantaneous luminosity‣ Illuminates entire calorimeter

Total Noise (electronic+pile-up) @ 1034 cm-2s-1(most significant in forward)‣ Major contributions from out-of-time
signal due to calorimeter shaping
functions‣ Ranges from ~20 MeV (central) up to
about ~10 GeV (forward) per cell7

Calorimeter Signal : Towers
Impose regular grid view on event‣ Δη × Δφ = 0.1 × 0.1‣ Motivated by event ET flow
‣ Natural choice for trigger
Sum cell signals in tower‣ Include all cells (no selection)→ Significant contribution fromnoise cells‣ Sum typically includes geometrical weight8

Calorimeter Signal : Topological Clusters
Cluster energy deposits in 3-D topologically connected cells‣ Clustering over different sampling layers and calorimeters‣ Based on cell energy significance in units of σnoise = σelectronics‣ Correlated signals taken into accountσpile-up

Topo. clusters in 1st FCal layer(Jet with pT>50 GeV)

Threshold (default)
Cluster seed :|Ecell|/σnoise > 4

Neighbor cells :|Ecell|/σnoise > 2→ Best performance in test beam
→ Can include very small signal cells
due to neighboring cell signals

Cells along the perimeterof the cluster : |Ecell|/σnoise > 0

Topological cluster studies with cosmic data
→ H. Okawa, JPS meeting 26aZE5, March 26, 20089

Jets in ATLAS

10

Jet Finders in ATLAS
Cone Jet Finders

Dynamic Angular Distance Jet Finders

 Seeded fixed cone

 Kt algorithm‣ Iterative cone finder starting from seeds‣ Free parameters:- seed ET threshold (typically 1 GeV)- cone size R (=0.4 or 0.7)
‣ Split and merge with overlap fraction
threshold of 50%‣ No infrared or collinear safe

‣ Combines proto-jets if relative pT is smaller
than pT of more energetic proto-jet‣ No seeds needed‣ Fast implementation available

 Alternative approaches- Aachen/Cambridge algorithm‣ Similar to Kt, but only distance between

 Alternative approaches

objects considered (no use of pT)- Seedless cone

‣ Each input is seed (theoretically favored)‣ Infrared and collinear safe
‣ Need split and merge- MidPoint cone

- Optimal Jet Finder‣ Based on the idea of minimizing a test
function sensitive to event shape‣ Use unclustered energy in jet finding‣ Place new seed between two cones‣ Need split and merge‣ Improved infrared and collinear safe

Seeded fixed coneand Kt are popularalgorithms at ATLAS
11

Kt

Cone

Tower-Based Jet‣ Collect all EM energy cell signals into projective towers
No explicit use of longitudinal readout granularity in jet finding Input signal = “Uncalibrated” EM energy scale‣ Cancel noise by re-summation of the towers


Towers with E<0 are added to near-by towers with E>0 until the
resulting proto-jet has E>0 (all cells are kept!)‣ Run jet finding on the proto-jets‣ Apply “global” cell level calibration (→next slide)

Retrieve all cells used in the jet Apply cell level calibration weights (derived from MC) as a function ofcell energy density and cell location
 Obtain hadronic energy scale jets with dead material corrections‣ Apply residual corrections for (ET, η) and jet algorithm
dependence

Results are physics jets calibrated at particle level
12

Global Hadronic Calibration

density and calorimeter region by minimizing

χ2 =

(

Nevents Njets

∑

∑

)

EjetReco − EjetTruth
EjetTruth

where
Reco

Ejet

Cells

= ∑ wi (ρi, Xi) ∙ Ei
i

()

Ei
ρi =
Vi

wi

Derived from QCD cone 0.7 jets in Pythia dijet production‣ Cover wide pT range from ~10 GeV to a few TeV
cell weights vs cell energy density
wi 2
For calorimeter tower jet
emb0
emb1
‣ Find a matched (stable interacting) particle jet
1.8
eme0
eme1
Extract
cells
from
the
tower
jet
‣
‣ Determine cell weights as a function of cell energy 1.6
1.4
1.2
1
2

EM cal3456E(GeV)log(
3 )V(mm )

789cell density

Ei : cell energy (EM scale)
Vi : cell volume
Xi : cell location
wi : cell calibration weights‣ Correct residual (ET,η)-dependent signal variations after cell weights are fixed➔ This is called H1 calibration

13

Tower Jet : Performance
1.2

Seeded Cone 0.7
Cone "R=0.7 0.00<!<0.50

1.15

Cone "R=0.7 1.50<!<2.00

1.1

1.05

1

EReco/ETruth

EReco/ETruth

EMeasured / ETrue

Scale

1.2

0.95

0.95

0.9

0.9
0.85

3

10

Truth

E

0.2

0.22

!Reco/EReco!Reco/EReco

σMeasured / EMeasured

102

0.2

T

102

[GeV]

0.00<"<0.50 : a=0.65 b=0.03 c=4.89

0.18
0.16

0.16

0.14

0.14

0.12

0.12

0.1

0.1

0.1

0.08

0.08

0.06

0.06

0.04

0.04

0.04
102

0.02

3

10

3

10

ETruth [GeV]σ/E ~ 65%[∑ET]-1/2 + 3%
σ/E ~ 105%[∑ET]-1/2 + 2%

0.2

0.00<"<0.50 : a=0.69 b=0.03 c=6.30

0.18

1.50<"<2.00 : a=1.03 b=0.02 c=8.30

0.02

H1 calibration

1.05
1

0.22

0.0<η<0.5
K R=0.6 1.50<!<2.00
1.5<η<2.0
K T R=0.6 0.00<!<0.50

1.1

1

0.85

Kt 0.61.15

0.9

Resolution

1.2

1.50<"<2.00 : a=1.19 b=0.01 c=10.00

102

3

10

E
[GeV]
E
[GeV]
100
1000
100
1000
True Energy (GeV)
True Energy (GeV)‣ Similar stochastic and constant terms between two algorithms‣ Kt jets have ~30%(50%) larger noise term than cone jets for0.0<η<0.5 (1.5<η<2.0)
14
Truth

Truth

Tower Jet : Problems
Tower-based Jets
❖ Too many non-signal cells included in jet‣ Not only lateral but longitudinal sum with (large) fixed area
‣ Relatively large noise contribution❖ Input calibrated only at EM scale‣ Relative mis-calibration (>30%) between towers possible‣ Could produce “huge” jets (especially for Kt)

Cluster-based Jets
❖ Better control of noise level at input to jet❖ Allow to use “calibrated” input to jet finder‣ Local hadron calibration to topological clusters‣ Smaller relative mis-calibration expected (~5% or less)‣ Possible to select inputs like particle selection for truth jets
15

Local Hadron Calibration
1 dn
N dE

‣ Start with topological clusters‣ Cluster classification

E=100GeV, !, QGSP_BERTMC❖ Clusters classified as either EM,
hadronic or unknown, based on

E=87.57GeV"/E=12.7%

0.06

E=75.65GeV"/E=12.2%

0.04

E=99.60GeV"/E=8.5%

E=89.22GeV"/E=12.4%

EM-scaleweightedout-of-cluster
dead material- EM fraction- cluster shape information

0.02‣ Hadronic weighting❖ Derive and apply weights to cells
in hadronic clusters❖ Correct for energy deposited incalorimeter, but outside of cluster‣ Dead material corrections❖ Correct for energy lost in
uninstrumented regions of thedetector
Classification, weighting andcorrection algorithms all basedon single particle MC simulation

1 dn
N dE

‣ Out-of-cluster corrections

0

0.06

60

60

80

100

80

E=100GeV, !, DATA
Test
Beam DATA

E=72.32GeV"/E=13.3%

100

E=86.48GeV"/E=14.6%
E=89.59GeV"/E=14.4%

0.04

120

120

140

140
E [GeV]

EM-scaleweightedout-of-cluster
dead material

E=103.98GeV"/E=11.8%
0.02

0

60

60
16

80

80

100

100

120

120

140

140
E [GeV]

Energy (GeV)Cluster-Based Jet
Build jets from topological clusters
with local hadron calibration‣ All calibrations derived only
from single particle MC❖ No jet context bias (e.g, algorithm)‣ Jet calibration factorized at
input level❖ Contrast to CDF and D

wherefactorized corrections applied at
jet level‣ Control of systematics❖ Factorization allows to address

systematic uncertainties at various
levels somewhat independently

17Cluster Jet : Performance (I)8000

Number of Cells per Jet

Cone Tower Jets R = 0.7
Cone Topo Jets R = 0.7

12000
10000

#Cells per jet

Jet noise (MeV)

Noise (MeV)

Noise in jet vs jet |η|Electronics
noise only

6000
4000

#Cells per jet vs jet |η| Cone R=0.7
Cone Tower Jets R = 0.7
Cone Topo Jets R = 0.7
33 < pTJet < 67 GeV

104

10

3

Tower jet
Cluster jet

2000
0
0

0.5

1

1.5

2

2.5

3

3.5

4
Jet |!|

0

0.5

1

Jet |η|

2

2.5

3

3.5

4
Jet |!|

Jet |η|1 cluster corresponds to 1.6 truth particles

Ntopo cluster E > 1 GeV

1.5

Cluster jets:‣ much smaller noise‣ much fewer cells→ Is signal not killed?

300
140
120

250

100

200

80

150

60
100
40
50

20
0
0

50

100

150

200

250

Ntruth particle E > 1 GeV

0Linear correlation between truth particle
and reconstructed cluster multiplicities :‣ Pythia QCD di-jet (140<pTJet<280 GeV)
‣ ~1.6(1.1) clusters per particle in jet at
central (end-cap)➔ Should be better for isolated particles
18Cluster Jet : Performance (II)Cone "R=0.7 0.00<!<0.50
Cone "R=0.7 1.50<!<2.00

1.1

1.05

1

EReco/ETruth

EReco/ETruth

Tower Jets

1.15

1.2

1.05

0.95

0.95

0.9

0.9

0.9

3

10

Truth

E

0.2

0.85
0.22

!Reco/EReco

102

0.2

102

[GeV]

0.00<"<0.50 : a=0.65 b=0.03 c=4.89

0.18

0.14

0.14

0.12

0.12

0.1

0.1

0.08

0.08

0.06

0.06

0.04

0.04

0.04

102

0.02

3

10

ETruth [GeV]

1.50<"<2.00 : a=1.07 b=0.01 c=6.43

0.16

0.1

3

10

0.00<"<0.50 : a=0.64 b=0.03 c=4.26

0.18

1.50<"<2.00 : a=1.03 b=0.02 c=8.30

Seeded ConeR = 0.7
H1 calibration

σ/E ~ 65%[∑ET]-1/2 + 3%
σ/E ~ 105%[∑ET]-1/2 + 2%

0.2

0.16

0.02

0.0<η<0.5
Cone "R = 0.7 1.50<!<2.00
1.5<η<2.0
Cone "R = 0.7 0.00<!<0.50

1.1

1

0.85
0.22

Cluster Jets

1.15

1

!Reco/EReco

EMeasured / ETrue
σMeasured / EMeasured

Resolution

Scale

1.2

1.2

E
[GeV]
100
1000
True Energy (GeV)
Truth

102

3

10

E
[GeV]
100
1000
True Energy (GeV)
Truth‣ Similar stochastic and constant terms between two jet types‣ Cluster jets have ~13%(23%) smaller noise term than tower
jets for 0.0<η<0.5 (1.5<η<2.0)19

Jet Energy Resolution‣ Dijet balance‣ Kt balancePT(Jet)/P (Z) - 1

Z→ee

T

0.02
-0

-0.02

-0.04

-5 %

-0.06
-0.08
-0.1
-0.12
-0.14
-0.16
-0.18

Alpgen, 500 pb-1

Seeded0.7
Cone07 Conejets
Tower Jets
50

100

150

200

Alpgen, 120 pb-1
Pythia, 120 pb-1
250

300

350 400
PT Z (GeV)

pT(Z-boson) (GeV)!(pT)/pT

Jet Energy Scale‣ γ/Z - jet pT balance‣ W→jj using MW in tt (light quark)pT(Jet) / pT(Z) - 1

Validation with real data is
crucial as ATLAS calibration
scheme is MC basedσ(pTJet) / pTJet

Performance
Validation

0.04

0.2

MC Truth
Dijet balance
Kt balance

CONE 0.7 --- $ # = 2.8 --- |"| < 1.2

0.18

DiJet (SR correction)

0.16

kT balance
Truth

0.14
0.12

Seeded Cone 0.7
Tower Jets

0.1
0.08

- Based on Tevatron experience- Utilize pT balance between 2 jets
- Soft radiation effects taken into account

0.06
0.04
0.02
0

100

200

300

400 500 600 700 800
Average (p +p )/2 GeV(pTJet1+pTJet2)/2 (GeV)
T,1

20

T,2

Very High pT Jet‣ Very high pT jet in TeV range is an unexplored territory at collider experiment‣ Calibration challenging as O(TeV) pT is too high to use γ/Z - jet balance method➔ Exploring the technique to calibrate jets at TeV range

Option 1. Multi-jet Balance

Jet3

Calibrate jet1
using pT balance‣ Use QCD multi-jet events- ≥4 jets with pT>40 GeV- Jet pT cuts : e.g, 1000<pTJet1<1140 GeV, pTJet2<470 GeV- ∆ϕ(Jet1, Recoiling jets) > 160 degree‣ Evaluate jet1 energy scale from pTJet1/pTRecoiling Jets
‣ Possible to extend pT range by iteration

Jet2 Recoiling Jets

EventsEventsJet1

3

1 fb-1

PT1000 (1 fb-1)

2.5 1000<pJet1<1140 GeV/c
T
2

Potential accuracy of
1 TeV jet scale at 1 fb-1
< 10%

1.5
1
0.5

(comparable to low pT jets)

0
0.4

21

0.6

0.8

1

1.2

1.4

1.6

pJet1 / p!RemJets
T
T

pTJet1/pTRecoiling Jets (GeV)Very High pT Jet
Option 2. Track-based Method
Use ∆R 1/PTJet
to measure JES
Jet(dR Cone
=0.
4)
Pi

Jet
(dR Cone
=0.
4)

∆R

Pj

tracks in jet‣ Use QCD di-jet events- count all tracks inside the leading jet cone
- calculate ∆R values over all combinations forleading N tracks and take mean value‣ Complementary to multi-jet balance method‣ Need to study flavor (in)dependence!R∆R

low PT jetshigh PT jets

!R(Peak) : PYTHIA, Leading 2 Tracks

0.03!R(Peak) : HERWIG, Leading 2 Tracks

0.025

QCD di-jets
PYTHIAHERWIG

0.02

Potential accuracy of 1 TeV
jet scale at 1 fb-1
~20% level

0.015
0.01
0.005
200

400

600

800

1000

1200
TruthJet
pJet(GeV/c)True pT (GeV)
T

22

MET in ATLAS

23

MissET in ATLAS
Missing ET is an event variable representing ET of “invisible” particles~
χ,G,...
ν,~detected
particles

i=1

i=1

MissET = ∑ETi = − ∑ETi

(concept is simple...)Measure from calorimeter cells above noise threshold at ATLAS
CaloCells

MissEX(Y) = − ∑EX(Y)i, MissET = [MissEX2 + MissEY2]1/2
i=1‣ Correct for muons and energy loss in dead materials‣ Correct for hadronic jets with cell-level wights (H1 calibration)
Global Calibration‣ Apply global cell-level weights toall signal at once

Refined Calibration (default)‣ Identify physics objects in an event- e, γ, τ, jets, muons, unused topological clusters‣ Decompose objects into constituent cells‣ Calibrate cells with object calibration weights
24❶ Z→ττ❷ W→eν, µν❸ tt semi-leptonic❹ A→ττ (mA = 800 GeV)
❺ SUSY (~1 TeV mass)

MissET Resolution
Follow σ = a [∑ET]1/2 overa very wide range of ∑ET(True−Measured)/True
MissEX
Linearity of response

MissET Scale
Fairly robust around a few %over wide MissET range anddifferent processes

Scale

0.2
0.1

❸

❶

❺

❹

0
-0.1

❷

ET calibration
at EM
scale
GlobalCalibration
miss
ET global calibration
Refined Calibration
miss
ET refined calibration
miss

-0.2
-0.3
0

50

100

150

200

250
300
miss
True ET (GeV)

True MissET (GeV)
σ(True−Measured MissE
X) (GeV)Resolution(GeV)

MissET
Performance

0.3

40
QCD Jets
35
30
25

SUSY

Refined Calibrationtt
A #"" (→fit)

20
15
10

Fit = 0.57 [∑ET]1/2

5

Resolution

25

0
0

200 400 600 800 1000 1200 1400 1600 1800 2000
! ET (GeV)

Measured ∑ET (GeV)0.1 ∑ET bin

50 <" ET < 60 GeV
Offset = 0.13± 0.01
Sigma = 3.92± 0.00
#2/ndf = 16.74

50-60 GeV
0.08 80-120 GeV
200-240 GeV

80 <" ET < 120 GeV
Offset = 0.24± 0.01
Sigma = 5.21± 0.00
#2/ndf = 20.93587

0.06

200 <" ET < 240 GeV
Offset = 0.56± 0.03
Sigma = 7.68± 0.02
#2/ndf = 1.44923

0.04

miss
"(E
) (GeV)σ(MissE) (GeV)XX,Y

Normalised Events / (1 GeV)MET Validation : Mini. Bias
14
12
10

Refined Calibration

Fit = 0.52[∑ET]1/2
Jet range)
Dijets(0.52! 0.00)(p! ET# /ndf = 0.54
8-17 GeV
Minimum Bias
17-35 GeV
J0J135-70 GeV
J2
J3
70-140 GeV

8

T

2

6
4

0.02
0
-30

2

-20

-10

0

10 miss20
30!(EX,Y ) (GeV)MissE
X (GeV)

Minimum Bias

00

100

200

300

400

500
600
!ET (GeV)∑ET (GeV)‣ Check if various distributions are consistent with expectations❖ MissEX/Y , MissET, ϕ(MissET), MissEX resolution vs ΣET‣ Background rejection to be done

❖ Beam Gas, Beam Halo, Empty events❖ Any additional quality cut after Minimum Bias Trigger?

For details‣ Etmiss CSC notes, MinBias section by Anna Phan, Naoko Kanaya
MinBias WG Meeting, 10 Sep. 2007
‣ Hideki Okawa,
26

MET Validation : W→lν
W’s Transverse Mass : MT = [2 pTν pTμ (1 - cos∆ϕ)]1/2
Template Method‣ Fit MT distribution (in each ΣET range)

1.04

50 GeV < " |ET| < 70 GeV

18
16
14

1.02

Events!2/ndf

MEX Scale

20

700

12

MEX Reso = (0.53 ! 0.02) ! |ET| "(3.7 ! 0.4)[GeV]10

6

Estimation

Resolution
ATLAS Preliminary

10
8

0.98

6

50 GeV < ! |ET| < 70 GeV

600
500
400

Pseudo-data300

Template

200

4
0.96
3

ATLAS Preliminary
4

5
6
7
8
9
MEX Resolution [GeV]

2
0

100
0 40

ATLAS Preliminary
50

60

70

1.03

Estimation

1
0.99
0.98
0.97

Scale

0.96

ATLAS Preliminary
00 50 100 150 200 250 300 350 400 450 500 0.950 50 100150200250300350400450500! |ET| [GeV]! |ET| [GeV]

12
1

Pseudo-data

1.01
Pseudo-data

2

1.04
1.02

8

4

from real data with each template, anddetermine the parameters by minimizing
the χ2

1.05

14

MEX Scaletruth lepton pT and MET with 2 smearing
parameters: scale factor α & resolution σ→ MissEXSmeared =α TruthMissEX + Gauss(0,σ)

1 Isolated μ with pT>20 GeV, |η|<2.5 & MissET>20 GeV
MEX Resolution [GeV]‣ Use MT templates constructed from MC

W→µν @ 28 pb-1

80

90 100 110 120
27MT [GeV]‣ Resolution well reproduced‣ Scale reproduced within ~1 %‣ Similar results for W→eν channel
For details:
ETmiss CSC note, Early data - W→lν
section by Hideki Okawa & Naoko Kanaya

Energy Flow Approachfor Improvement28

Energy Flow
‣ Use sub-detectors to reconstruct energy(particle) flow in the detectors‣ Main use : low pT jet resolution
track-calorimeter jet association
hadronic τ identification“Ultimate” Particle Energy Flow
Identification& Calibration
SW clusters w/ tracks→e
SW clusters w/o tracks→γ
muon hits w/ tracks→μ
combined clusters w/ τID → τ
topo. clusters w/ tracks→ h±topo. clusters w/o tracks → h0
Reconstruction

PTJet = ∑PTe,γ,µ,h±,h0,...
29

MET = − ∑PTe,γ,µ,h±,h0,...

Energy Flow Implementation‣ Extrapolate tracks to calorimeter to find matched cluster(s)‣ If matched cluster found, subtract expected energy deposit‣‣‣(obtained from single particle MC) from the cluster
Subtraction performed using cell removal or adjusting cluster energy
If Eclus(before subtraction) < Eexpected - k2σexpected, use cluster instead of track
If Eclus(after subtraction) < k1σexpected, the cluster is discarded

0.2

0.2

0.18

0.18

0.16

0.16

0.14

Topocluster jets (H1)
Topocluster jets (Local Hadron)

0.14

0.12

0.12

0.1

0.1

0.08

* Energy Flow

Jet Resolution Vs True Jet Pt!(Pt)/Pt!(Pt)/Pt

Relative resolution

QCD dijet
eventsJet Resolution Vs True Jet Pt

40

50

60

70
80
90
True Jet Pt (GeV)

100

110

1200.08

40

50

60

True Jet
pT (GeV)
30

70
80
90
True Jet Pt (GeV)

100

110

120

Energy Flow : Hadronic W in Top
Apply energy flow algorithm to hadronic W decay in all hadronic tt events‣ Select jets from W → jj by looping over all jet combinations and minimizing√(

mWReco − mWPDG 2

mtopReco − mtopPDG

σWPDG

σtopPDG)+(

2)‣ Select ≥4 jets with pT > 50 GeV, MissET > 40 GeV
EnergyCone 0.4Flow
Hadronic W Mass (Energy Flow)

Topocluster (Local Hadron)
35

Mean
RMS
!2 / ndf
Constant

30

20

Mean
Sigma

W Mass (LCTopo)
Entries175
Mean
8.017e+04
RMS
5745!2 / ndf
34.02 / 17
Constant
23.76 ± 3.48
Mean
8.037e+04 ± 358
Sigma4035 ± 493.4

175
7.981e+0430

5571
36.2 / 1733.14 ± 5.37
8.024e+04 ± 268
2841 ± 392.5

Number of Events

Number of Events

40

Cone 0.4

EntriesCone 0.4 Hadronic W Mass (H1 Weighted TopoClusters)25
20

σ=3.5±0.5 GeV

Entries175
Mean
8.003e+04
4975
RMS!2 / ndf
21.68 / 18
40.22 ± 4.99
Constant
Mean
8.074e+04 ± 209
Sigma2585 ± 243.54030

σ=5.0±0.6 GeV

15

W Mass (H1Topo)50

Number of Events

W Mass (EFlow)
50

Topocluster (H1)

Cone 0.4 Hadronic W Mass (Local Hadron TopoClusters)20

σ=3.2±0.3 GeV

10
10

10

5

×103
×1030
×1003
0
0
20
40
60
80
100
120
140
160
0
20
40
60
80
100
120
140
160
0
20
40
60
80
100
120
140
160
Weighted TopoCluster Hadronic W Mass Using Cone 0.4 Jets (MeV)
Local Hadron TopoCluster Based Hadronic W Mass Using Cone 0.4 JetsH1(Me
Energy Flow Based Hadronic W Mass Using Cone 0.4 Jets (MeV)

Hadronic W mass (GeV)

Better angular resolution for energy flow jets than local hadron topocluster jets

➔ Improves W mass resolution over local hadron topocluster jets
31Track Jet
Different jet finding strategy based on
3-D (η,φ,Z) clustering using track info
Lots of useful applications‣ Track-based energy scale corrections- Charged particle content correlatedwith calorimeter jet response‣ Jet identification- Jets from minimum bias pile-up events- Jet charge (?)‣ Fake missing ET clean-up- Jets in calorimeter cracks‣ Low pT jet reconstruction

32

Track Jet : Jet Energy Resolution
Uncorrected
0.0<ftrk<0.4
0.9<ftrk<1.0

Resolution

6.6 GeV

Corrected
6.1 GeVETReco - ETTruth (GeV)

Add information fromtracker to improve
resolution after JES
corrections‣ E.g, ftrk = ΣpTTrack/pTJet
fraction of jet pT carried bycharged tracks

33

Track Jet : Pile-up Rejection‣ Soft pile-up noise → Topological Clustering‣ “Hard” (minimum bias jet) pile-up?
➔ 3-D (η,ϕ,Z) jet finding using tracks- Associate jets to primary vertices- Evaluate fraction F of charged trackenergy in each jet originating in eachidentified primary vertex

Minimum bias events do make hard jets!
tt events with pile-up
Jet2

Reconstructed
tt vertex
Jet1

All jets
F > 0.75
F < 0.50

Jet4
Reconstructed MB vertex
Jet334

Track Jet : Fake MET Rejection
QCD di-jet (140<pTJet<280 GeV)

‣ Select events where MET

pointing to any problematic
calorimeter regions in ϕ

‣ See if any track-jet isreconstructed along thedirection withoutaccompanying calorimeterjet35Summary
No real summary as performance studies are on
going, while ...

‣ Jet and Missing ET performance at ATLAS looks
very promising over wide kinematic range anddifferent processes‣ Most ATLAS calibrations and corrections are
based on Monte Carlo simulations➔ Validation of detector simulation andcalibration with real data is very crucial‣ Many (unexpected) challenges ahead of us, but

being ready to attack problems with useful tools
36