Particle Jets

1P. Loch

U of ArizonaAugust 3, 2011

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Particle Jets

Collection of particles from common source Several sources in each collision

Hard scattering, multiple parton interactions in the underlying event, initial and final state radiation Describe the simulated collision viewed with a microscope (idealized)

Microscope technology – jet finding algorithm Resolution – ability of a jet finder to (spatially) resolve jet structures of collision, typically a

configuration parameter of the jet finder Sensitivity – kinematic threshold for particle bundle to be called a jet, another configuration parameter

of the jet finder

2P. Loch


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Usefulness of Particle Jets

Good reconstruction reference for detector jets Provide a truth reference for the reconstucted jet energy and momentum

E.g., can be used in simulations together with fully simulated detector jets to calibrate those (we will follow up on this point later!)

Extract particle jets from measurement by calibration and unfolding signal characteristics from detector jets Understand effect of experimental spatial resolution and signal thresholds at particle level

Remember: electromagnetic and hadronic showers have lateral extension → diffusion of spatial particle flow by distributing the particle energy laterally!

Remember: noise in calorimeter imply a “useful” signal threshold → may introduce acceptance limitations for particle jets!

Good reference for physics Goal of all selection and unfolding strategies in physics analysis

Reproduce particle level event from measurement as much as possible! Require correct simulations of all aspects of particle spectrum of collision right

Matrix element, parton showers, underlying event (non-pertubative soft QCD!), parton density functions,… Parton shower matching to higher order matrix calculation in complex pp collision environment is a hot topic

among theorists/phenomenologists today!

Allow to compare results from different experiments Specific detector limitations basically removed Also provides platform for communication with theorists (LO and some NLO )

Important limitations to be kept in mind NLO particle level generators not available for all processes (more and more coming) NNLO etc. not in sight

3P. Loch


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What Is Jet Reconstruction, Then?

Model/simulation: particle jet Attempt to collect the final state particles described above into objects

(jets) representing the original interaction features/parton kinematic Re-establishing the correlations induced by the common source at a given

spatial resolution Experiment: detector jet

Attempt to collect the detector signals from these particles to measure their original kinematics

Usually no attempt to reconstruction a (very model/order of calculation dependent) parton level!

4P. Loch


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Image of Jets in the Detector

5P. Loch


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Detector Effects On Jets

—

+

—

+

+—

+

100 MeV

10 GeV

1 GeV

η

φ

Change of composition Radiation and decay inside

detector volume “Randomization” of original

particle content Defocusing changes shape in

lab frame Charged particles bend in

solenoid field Attenuation changes energy

Total and partial loss of soft charged particles in magnetic field

Partial and total energy loss of charged and neutral particles in inactive upstream material

Hadronic and electromagnetic cacades in calorimeters Distribute energy spatially Lateral particle shower overlap

6P. Loch


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+

—

+—

—

+

+









100 MeV

10 GeV

1 GeV

η

φ

7P. Loch


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+

—

+—

—

+

+









100 MeV

10 GeV

1 GeV

η

φ

8P. Loch


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+

—

+—

—

+

+









+—

—

+

+

100 MeV

10 GeV

1 GeV

η

φ

9P. Loch


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Total loss of soft charged particles in magnetic field



+—

—

+

+

+—

—

+

+

c

c

c

c

c

c

c

c

c

c

c

c

100 MeV

10 GeV

1 GeV

η

φ

10P. Loch


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Calorimeter Acceptance In Magnetic Field

Single particle response modification Magnetic field in front of calorimeter

Charged particles may not reach calorimeter at all

calo 0E

T 0.5 GeVp

11P. Loch


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Acceptance



2 2caloE p m

T0.5 1 GeVp

12P. Loch


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Acceptance



2 2caloE p m

T1 2 GeVp

13P. Loch


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Acceptance



2 2caloE p m

T 2 GeVp

14P. Loch


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Jet ReconstructionChallenges & Tasks

15P. Loch


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Jet Reconstruction Challenges

Experiment (“Nature”) Jet Reconstruction Challenges

physics reaction of interest (interaction or parton level)

added tracks from underlying eventadded tracks from in-time (same trigger) pile-up event

jet reconstruction algorithm efficiency

longitudinal energy leakagedetector signal inefficiencies (dead channels, HV…)

pile-up noise from (off- and in-time) bunch crossingselectronic noise

calo signal definition (clustering, noise suppression…)dead material losses (front, cracks, transitions…)

detector response characteristics (e/h ≠ 1)jet reconstruction algorithm efficiency

lost soft tracks due to magnetic field

16P. Loch


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Experiment (“Nature”)










jet calibration task is to unfold all this to

reconstruct the particle level jet driving the

signals…

17P. Loch


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signals……modeling and calculations establish the link between particle and

interaction level…

18P. Loch


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signals……modeling and calculations establish the link between particle and

interaction level… …but how is this really done?

19P. Loch


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Jet Reconstruction Task

Experiment (“Nature”) The experiment starts with the actual collision or the generator… Triggered collision with signal parton collision, fragmentation &

underlying event (experiment), or: Interaction level calculation with fragmentation and underlying

event modeling (simulations) … go to the particles in the simulation …

Here particle level event represent the underlying interaction and the full complexity of the physics of the collision in the experiment

… collect the detector signals … From the readout (experiment), or: Take the stable (observable) particles and simulate the signals in

the detector (e.g., the calorimeter and tracking detector)(simulations)

… and compare them! Complex – need to include all experimental biases like event

selection (trigger bias), topology and detector inefficiencies This establishes particle jet references for the detector

jets! Of course only in a statistical sense, i.e. at the level of

distributions!

20P. Loch


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Particles

2 2( , )pdf Q x

UEMB

MB MB

Multiple Interactions

Stable Particles

Decays

Jet Finding

Particle JetsGeneratedParticles

Modeling Particle Jets

21P. Loch


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Modeling Calorimeter JetsExperiment (“Nature”)Reconstructed

Jets

Stable Particles

Raw Calorimeter SignalsDetector Simulation

Reconstructed Calorimeter SignalsSignal Reconstruction

Jet Finding

Identified Particles

22P. Loch


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Measuring Calorimeter JetsExperiment (“Nature”)Reconstructed

Jets

Observable Particles

Raw Calorimeter SignalsMeasurement

Reconstructed Calorimeter SignalsSignal Reconstruction

Jet Finding

Identified Particles

23P. Loch


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Jet Calibration

What is jet calibration? Straight forward: attempt to reconstruct a measured jet such that its final four-momentum is close to

the true jet kinematics generating the signal Why is it needed?

Could compare simulated and measured calorimeter signals at any scale and deduct the true kinematics from the corresponding particle jet in simulation

Remember energy scales in calorimeters? But need to reconstruct any jet in the experiment

Even (or especially) the ones in events we have not simulated – which probably means new physics? To understand these events the best measurement of the true jet independent of the availability of

simulations for this specific event – no simulation bias allowed in general! Can we calibrated without simulations at all?

Complex physics and detector environment – hard to avoid simulations for precision reconstruction! But there are in-situ jet calibrations So jet reconstruction needs to include a calibration

Use a simulated calibration sample representing simple final state Chose a somewhat understood Standard Model topology like QCD di-jets

Calibrate using measurable jet features Establish functions using jet observables as parameters to calibrate calorimeter jets from a basic scale to

the final jet energy scale If done right, simulation biases can be reduced, especially concerning the correct simulation of the event

topology Understand the limitations (systematic error) in the context of the analysis

All this is the global subject of the remaining lectures!

24P. Loch


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Global Hadronic Energy Scale

25P. Loch


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Global Calibration Techniques

Use jet context for cell calibration Determine cell weights using jet energy constraints

Same principle idea as for local cell weighting, but different global energy scale

Needs jet truth reference Jet context relevant

Supports assumption of hadronic signal activity Has enhanced electromagnetic component contributing

to the weighting function parameterizations of all cells – larger (volume/area) context than topological clustering

May be biased with respect to calorimeter signal definition and jet algorithms

Jet energy references for calorimeter jets Simulation

Matching particle level jet (same jet definition) energy Experiment

pT balance with electromagnetic system like photon or Z-boson

W mass spectroscopy

Sampling energy based jet calibration Coarser than cell signals but less numerical complexity

Fewer function parameters

26P. Loch


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Truth Jet Matching

Simulated particle jets Establish “true” energy reference to constrain calibration function fits for

calorimeter jets Attempt to reconstruct true jet energy

Need matching definition Geometrical distance Isolation and unique 1-to-1 jet matching

2 2particle,jet rec,jet particle,jet rec,jet( ) ( )

R

27P. Loch


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Global Calibration Fits Using Simulations

Select matched jet pair Typically small matching radius

Rmatch = 0.2 – 0.3

Restrict jet directions to regions with good calorimeter response No excessive dead material Away from cracks and complex

transition geometries

Calibration functions Cell signal weighting

Large weights for low density signals

Small weights for high density signals

Sampling layer signal weighting Weights determined by longitudinal

energy sharing in calorimeter jet Functions can be complex

Often highly non-linear systems

Example of calorimeter regions to be considered for jet calibration fits in ATLAS (tinted green). The red tinted regions indicate calorimeter cracks and transitions. The points show the simulated jet response on electro-magnetic energy scale, as function of the jet pseudorapidity. (figure for illustration purposes only!)

28P. Loch


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Rmatch = 0.2 – 0.3









rec,cell cell cell 0,cell

cell c

cell

cel

ce

l cel

ll cell

l

e l

c

l

e

l

l

(avoid

(avoid boosting

suppress

noise

ing em

!)

respo

m

min

( , )

( , )

Typical bou

ax( ( , )ndary condi

) 1.5 3

for

( ( ,

for

))

.0

1

io s:

.0

t n

w

w

E w E

w

cell

cell

cell 1cell cell cell

cell

cell ce

(similar in ATLAS)

is a region descriptor for a given cell,

lik

nse

e

!)

log

Example for non-algebraic functional form:

for ( ) log( ) log( )

( ,

,

) i iij

j

S

w

M

ll

calorimeter module id,sampling id

0 0

cell signal weights , parameterized as function

of the cell energy and the clust

Exampl

er en

e

y

:

erg

Wk clE E

29P. Loch


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Rmatch = 0.2 – 0.3









rec, 0, 0,cellcells in

sampling

0,celljet

EM

cells in

EMC EMC0,cell

all jet cells

C ,

Possible parameterizations:

( ), with

Example for non-algebraic functional form: fo( ) r

S S S S

S

EMCS

S i

S

Sw f

E w E w E

E

w w ffE

, , 1 EMC i EMC EMC iF f F

30P. Loch


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Fitting Possible constraints

Resolution optimization Signal linearity Combination of both

Regularization of calibration functions Try to linearize function ansatz Use polynomials Can reduce fits to solving

system of linear equations

Non-linear function fitting Use numerical approaches to

find (local) minimum for multi-dimensional test functions (e.g., software like MINUIT etc.)

rec,jet cell cell cell 0,cellcells in jet

2

rec,jet particle,jet22 2

matching rec,jet particle,jetjet pairs

Reconstructed jet energy with cell calibration:

Fit such that...

Rec

( , )

onstr

mi

u

n

cte

ij

E w E

E E

2 1r

2

re

ec,jet r

c,jet EM

ec,jet

C 0, in jet

,

d jet energy with sampling calibration:

Fit using the same test function!

Note that

)

!

(

i

S

S

SS

E w f E

E

31P. Loch


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Attempted de-convolution of signal contributions Normalization choice convolutes various jet response features

E.g., cell weights correct for dead material and magnetic field induced energy losses, etc.

Limited de-convolution Fit corrections for energy losses in material between calorimeter modules with different functional form Separation in terms, but still a correlated parameter fit

rec,jet cell cell cell 0,cell DM,jetcells in jet

2


m

2

atching rec,jet particle,jetjet p


Use test function such

( , )

that...

E w E E

E E

airs

2

0, 0, 0, particle,jet

2 2matching rec,jet particle,jetjet pairs

DM,jewith empirically motivated ansatz

( , )

nr

ifo

m

cell cell cell cell S before S behindcells in jet

w E E E E

E

t

cell

before

for dead material between

sampling layers a combinedbehin fit nd , in a of ,dS S W

32P. Loch


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Attempted de-convolution of signal contributions Normalization choice convolutes various jet response features

E.g., cell weights correct for dead material and magnetic field induced energy losses, etc.

Limited de-convolution Fit corrections for energy losses in material between calorimeter modules with different functional form Separation in terms, but still a correlated parameter fit

rec,jet cell cell cell 0,cell DM,jetcells in jet

2


m

2

atching rec,jet particle,jetjet p


Use test function such

( , )

that...

E w E E

E E

airs

2

0, 0, 0, particle,jet

2 2matching rec,jet particle,jetjet pairs

DM,jewith empirically motivated ansatz

( , )

nr

ifo

m

cell cell cell cell S before S behindcells in jet

w E E E E

E

t

cell

before

for dead material between

sampling layers a combinedbehin fit nd , in a of ,dS S W

Relatively low level of factorization in this

particular approach with correlated (by combined

fit) parameters!

33P. Loch


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Jet Inputs & Images

34P. Loch


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Experimental Jet Finder Input In ATLAS

Calorimeter towers Noise-suppressed towers

Cells in towers from topological clusters EM scale only

Photon/hadron response imbalanced during jet formation

Least algorithm bias

Calorimeter cell clusters EM scale option

Same as for tower input Provide calibrated jet finder input

Local hadronic scale balances responses better during jet formation in recursive recombination algorithms like Anti-kT

and kT

Reconstructed tracks Charged stable particles only

Resulting jets are incomplete Very useful for characterization of

calorimeter jet Large charged pT fraction indicates

hadron-rich jetDrawings by R. Walker (Arizona), inspired by K. Perez (Columbia)

Calorimetertowers filled with

cells from topological clusters

applies noise suppression to

tower signal

Topological calorimeter cell clusters locate

“blobs of energy” inside the detector following shower and particle flow

induced signal structures

35P. Loch


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Image Of Jets In Calorimeter

36P. Loch


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Jet Response & Calibration

37P. Loch


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Calorimeter Jet Response

Calorimeter jet response Electromagnetic energy scale

Available for all signal definitions No attempt to compensate or

correct signal for limited calorimeter acceptance

Global hadronic energy scale All signal definitions, but specific

calibrations for each definition Calibrations normalized to

reconstruct full true jet energy in “golden regions” of calorimeter

Local hadronic energy scale Topological clusters only No jet context – calibration

insufficient to recover calorimeter acceptance limitations – no corrections for total loss in dead material and magnetic field charged particles losses)

0,tower particletowers in particles in

jet jet0,jet

0,jet 0,tower particletowers in particles i

jet

reconstructed calorimeter jet

Unbiased and noise-suppressed towers:

E EEp p p

0,clusterclusters in

jet0,jet

0,jet 0,clusterclust

njet

matched particle

ers injet

reconstructed ca

jet(truth reference)

lorimeter jet

Topological cell clusters:

EEp p

particleparticles in

jet


jet

matched particle

2 2jet jet jett owers clusters

jet(truth reference)

Note at any scale:

0 for , 1m E p N N

E

p

38P. Loch


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cell cell 0,cell DMcells in

jetrec,jet

0,jetrec,jetcell cell 0,cell DM

cells in 0,jetjet

Cell based calibration for all calorimeter signals and jets in "golden spot":

( , )

( , )

w E E

Epp w p Ep



jet


jet

matched particle jet(truth reference)

(cells are extracted fr

E

p

om unbiased or noise suppressed

towers or topological clusters forming the jet)

39P. Loch


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rec,cluster particleclusters in particles in

jet jetrec,jet

rec,jet rec,cluster particletowers in p

jet


Locally calibrated clusters only:

E EEp p p

articles in

jet

matched particle jet(truth reference)

40P. Loch


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Jet Energy Scale

Final Jet Energy Scale (JES) Final jet calibration

All corrections applied

Best estimate of true (particle) jet energy Flat response as function of pT Uniform response across whole calorimeter

Relative energy resolution Depends on the calorimeter jet response – calibration applies compensation corrections

Resolution improvements by including jet signal features Requires corrections sensitive to measurable jet variables Can use signals from other detectors

Determination with simulations Measure residual deviations of the calorimeter jet response from truth jet energy

Derive corrections from the calorimeter response at a given scale as function of pT (linearity) and pseudorapidity (uniformity) for all particle jets

Use numerical inversion to parameterize corrections Conversion from truth variable dependence of response to reconstructed variable response

41P. Loch


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Jet Calibration in ATLAS (2010+ Data Summary)

42P. Loch


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Default 2010 ATLAS Jet Calibration (1)

Default for first data focuses on “simplicity” EM scale + additional corrections

Least algorithmic impact & sensitivity to modeling details Very few correction levels

Basic EM scale independently validated with data from Z → ee

Basic systematic uncertainty in most calorimeter regions derived independently from jet response

No significant improvements in resolution expected Calibration uses only average event environment and jet

reponse features

More dynamic calibrations under commissioning for 2011+ data Use hadronic calorimeter scales and jet features

GCW, LCW, GS

Expect jet energy resolution improvements Corrections are applied jet by jet

EM,cluster

clustersEM EM

0EM EMjet clusterE

E Ep p

EM Scale Jet

See also D. Schouten’s talk!

43P. Loch


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(Par

t III) Calibration sequence for EM scale jets

(1) Pile-up correction from data Average additional energy from pile-up is subtracted


TAnti-k

0.6R

Derived from minimum bias data by measuring:

T

PV( , )E

N

DATA

EM T jetEM+PU

EM+PU EM T jet EM EMjet jet

( ) cosh

( ) cosh

E E AEp E E A E p

EM,cluster

clustersEM EM

0EM EMjet clusterE

E Ep p

EM Scale Jet

ATLAS-CONF-2011-030

44P. Loch


Expe

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uctio

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nd je

t phy

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t the

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(Par


(1) Pile-up correction from data (2) Vertex correction from data to improve angular

resolution and pT response

Jet and constituent directions recalculated from reconstructed primary event vertex


Only jet constituents and jet direction re-calculated after vertex

shift – jet energy unchanged!

DATA

DATA

EM,cluster

clustersEM EM

0EM EMjet clusterE

E Ep p

EM Scale Jet

EM+PU EM+PU

EM+PU+Vtx EM+PU vertexjet jet

E Ep p X

EM T jetEM+PU


( ) cosh

( ) cosh

E E AEp E E A E p

45P. Loch


Expe

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spec

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reco

nstr

uctio

n a

nd je

t phy

sics a

t the

LHC

(Par




(3) Response calibration with MC truth jet

Match MC particle jet with simulated calorimeter jet

Restores calorimeter jet energy to particle

jet reference for given jet finder

configuration, physics and detector

response modeling


DATA

DATA

EM,cluster

clustersEM EM

0EM EMjet clusterE

E Ep p

EM Scale Jet

MC

EM+PU EM+PU


E Ep p X

EM+PU+Resp EM+PU 1

jet EM+PU detEM+PU+Vtx+Resp EM+PU+Vtx jetjet

( , )E E

Ep p

Corrects for detector effects & acceptance – parameterized as function of the original calorimeter jet direction ηdet and EM scale energy after pile-up correction

EM T jetEM+PU


( ) cosh

( ) cosh

E E AEp E E A E p

46P. Loch


Expe

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spec

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reco

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uctio

n a

nd je

t phy

sics a

t the

LHC

(Par

t III)


DATA

DATA

EM,cluster

clustersEM EM

0EM EMjet clusterE

E Ep p

EM Scale Jet

MC

EM+PU EM+PU


E Ep p X

Calibration sequence for EM scale jets (1) Pile-up correction from data (2) Vertex correction from data to improve angular


(3) Response calibration with MC truth jet

EM+PU+Resp EM+PU 1


( , )E E

Ep p

ATLAS-CONF-2011-032

ATLAS-CONF-2011-032

EM T jetEM+PU


( ) cosh

( ) cosh

E E AEp E E A E p

47P. Loch


Expe

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nd je

t phy

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LHC

(Par




(3) Response calibration with MC truth jet (4) Final direction correction from MC

Small correction to reduce bias in direction measurement

Introduced by poorly instrumented transition regions in calorimeter


DATA

DATA

EM,cluster

clustersEM EM

0EM EMjet clusterE

E Ep p

EM Scale Jet

MC

MCCorrection parameterized as

function of detector jet direction and energy

EM+PU+RespEM+JES

EM+PU+Vtx+RespEM+JES jet jet

EEpp

EM+JES Jet

EM+PU+Resp EM+PU 1


( , )E E

Ep p

EM+PU EM+PU


E Ep p X

EM T jetEM+PU


( ) cosh

( ) cosh

E E AEp E E A E p

48P. Loch


Expe

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spec

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reco

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uctio

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nd je

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(Par




(3) Response calibration with MC truth jet (4) Final direction correction from MC


DATA

DATA

EM,cluster

clustersEM EM

0EM EMjet clusterE

E Ep p

EM Scale Jet

EM+PU EM+PU


E Ep p X

MC

MC

EM+PU+Resp EM+PU 1


( , )E E

Ep p

ATLAS-CONF-2011-032

EM+PU+RespEM+JES

EM+PU+Vtx+RespEM+JES jet jet

EEpp

EM+JES Jet

EM T jetEM+PU


( ) cosh

( ) cosh

E E AEp E E A E p

49P. Loch


Expe

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spec

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uctio

n a

nd je

t phy

sics a

t the

LHC

(Par

t III) Calibration sequence for scale jets

1) Starting from locally calibrated (LC) clustersDetector effects unfolded to larger part

2) Pile-up correction from

dataVery different in presence

of pile-up history

(50 ns bunch-xing, strategy not yet confirmed)

3) Vertex correction from data to improve angular resolution and pT response

Very similar

4) Response calibration with MC

truth jet Expected to be significantly smaller due to hadronic energy scale input similar

5) Final direction correction from MCVery similar

Expectations for 2011 Data

DATA

DATA

LC,cluster

clustersLC LC

0LC LCjet clusterE

E Ep p

HAD Scale Jet

LC+PU LC+PU

LC+PU+Vtx LC+PU vertexjet jet

E Ep p X

MC

MC

LC+PU+Resp LC+PU 1jet LC+PU det

LC+PU+Vtx+Resp LC+PU+Vtx jetjet

( , )E E

Ep p

LC+PU+RespLC+JES

LC+PU+Vtx+RespLC+JES jet jet

EEpp

EM+JES Jet

LC T,LC jetLC+PU

LC+PU LC T,LC jet LC LCjet jet

( ) cosh

( ) cosh

E E AEp E E A E p

????

50P. Loch


Expe

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spec

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f jet

reco

nstr

uctio

n a

nd je

t phy

sics a

t the

LHC

(Par

t III) Calibration sequence for scale jets

1) Starting from locally calibrated (LC) clustersDetector effects unfolded to larger part

2) Pile-up correction from

dataVery different in presence

of pile-up history

(50 ns bunch-xing, strategy not yet confirmed)

3) Vertex correction from data to improve angular resolution and pT response

Very similar

4) Response calibration with MC

truth jet Expected to be significantly smaller due to hadronic energy scale input similar

5) Final direction correction from MCVery similar

Expectations for 2011 Data

DATA

DATA

LC,cluster

clustersLC LC

0LC LCjet clusterE

E Ep p

HAD Scale Jet

LC+PU LC+PU

LC+PU+Vtx LC+PU vertexjet jet

E Ep p X

MC

MC

LC+PU+Resp LC+PU 1jet LC+PU det

LC+PU+Vtx+Resp LC+PU+Vtx jetjet

( , )E E

Ep p

LC+PU+RespLC+JES

LC+PU+Vtx+RespLC+JES jet jet

EEpp

EM+JES Jet

LC T,LC jetLC+PU

LC+PU LC T,LC jet LC LCjet jet

( ) cosh

( ) cosh

E E AEp E E A E p

I prefer Cacciari/Salam/Soyez method!

51P. Loch


Expe

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t phy

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LHC

(Par

t III)

Jet Area Based Pile-up Correction

Principal idea (Cacciari/Salam/Soyez) Cluster the whole event as is with kT/CA

Resulting jets depend on distance resolution parameter

R = 0.4 seems to be ok No pT cuts on final jets, pT=0 jets allowed

Provide combined occupancy measure (~ # pT=0 jets) with transverse energy flow

Measure Et density for these jets event by event

Focus on low density regime Take median of all densities to reduce

dependence on hard part of event

Correction similar to tower based approach Use jet area of any hard jet to get pT offset

No need for hard jets to be clustered like for event decomposition

Use dynamic area definitions as given by FastJet

Correction derived from same event And event by event Seems to address jet response

dependence on number of primary vertices and (average) pile-up activity very well!

M.Cacciari, G. Salam, Pileup subtraction using jet areas, arXiv:0707.1378v2 [hep-ph]

52P. Loch


Expe

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(Par

t III)









Correction similar to tower based approach

Use jet area of any hard jet to get pT offset No need for hard jets to be clustered like

for event decomposition Use dynamic area definitions as given by

FastJet Correction derived from same event

And event by event Seems to address jet response


T,

event

detector

1

median

full detector

range/strip

j

j j

j

i j i

pA

Expect dependence of transverse energy flow on pseudo-rapidity from • Physics (see, e.g, Bjorken…) • Detector effects – signal loss in

calorimeter cracks cannot be corrected outside of e.g. jets (no good truth reference!)

53P. Loch


Expe

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n a

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(Par

t III)









Correction similar to tower based approach Use jet area of any hard jet to get pT offset

No need for hard jets to be clustered like for event decomposition

Use dynamic area definitions as given by FastJet

Correction derived from same event And event by event Seems to address jet response


G. Soyez, talk at workshop on Jet reconstruction and spectroscopy at hadron colliders, Pisa, Italy, April 18-19, 2011

TE A

54P. Loch


Expe

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(Par

t III)

Jet Reconstruction Validation & Uncertainties

55P. Loch


Expe

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(Par

t III)

Calibration Validation

Advantages of simulation based jet calibrations Explicit knowledge of “truth” allows understanding of jet signal

E.g., true jet energy and direction Factorization allows understanding the individual contributions to calibration

Analysis of (nearly) independent error sources and correction quality Improvements in complementary (orthogonal) corrections Note that while effects to be corrected may be correlated, the corrections are derived

independently (local hadronic calibration)

Challenges for simulation based calibration Need to validate simulation based calibrated signal with data

Single particle response – test beam and isolated charged tracks in collision events Effect of calibrations on signal in data and simulation – e.g., signal enhancement

Need to validate calibration inputs Same features in data and simulations – cell energy densities, cluster shapes, jet shapes

need to be well modeled Inside and outside of jets

Need to understand model dependencies Choice of calibration reference involves selection of physics models, detector simulation

parameters and geometries, quality of experimental effect modeling (e.g., noise) Variations/exchange of models needed to understand limitations introduced by chosen

reference

56P. Loch


Expe

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t the

LHC

(Par

t III)

Validation of Reconstructed Calorimeter Signals (1)

Basic calorimeter signal features enter hadronic calibration Cell energy density in signal weighting

Requires very similar distributions in cell energy densities Energy sharing between longitudinal calorimeter samplings

Validation of longitudinal shower development – correct energy sharing can enter calibration directly (parameter) and indirectly (classification)

More complex variables for cluster based calibration Cluster locations

Affects classification and following calibration method Cluster classification

Determines calibration strategy and corresponding signal boost Cluster isolation

Transfer from single particle calibration to jets Cluster density

Classification parametrization Cluster electromagnetic energy scale signal

Reference scale for cell weights

57P. Loch


Expe

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(Par

t III)

Jet Reconstruction Validation & Uncertainties

58P. Loch


Expe

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uctio

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nd je

t phy

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LHC

(Par

t III)

Quality Of MC Based Jet Calibration

Closure test for MC calibration Apply calibrations and corrections to MC

calibration sample Expected true energy or pT not perfectly

restored after all calibrations and corrections Residual non-closure is part of systematic

uncertainty of jet energy scale Differences in energy and pT linearity

Same correction factor applied Reconstructed (non-zero) jet masses do not

represent expected jet mass well – restoring only energy and direction leads to bias

(all plots from ATLAS-CONF-2011-032)

59P. Loch


Expe

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t phy

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LHC

(Par

t III)

Jet Energy Scale Uncertainties

(all plots from ATLAS-CONF-2011-032)

Contributions to systematic JES uncertainties in central region of ATLAS [MC] Non-closure of calibration

See previous slide [MC] model dependencies

Apply calibrations from reference sample to… Different response simulation/Geant4 shower model Detector description variations/material budget & alignment Alternative physics simulation with different

underlying event, fragmentation/hadronization, parton shower model…

[MC,data] calorimeter response Charged hadrons 0.5 < p < 20 GeV (see next slide)

E/p from isolated tracks in collisions Charged hadrons 20 < p < 350 GeV

Test beam experiments Basic energy scale and EM response

Z → ee in collisions Neutral hadrons

Estimates from MC (conservative) High energy particles in jet (p > 400 GeV)

Estimates from MC (conservative)

Extrapolation to end-cap and forward regions [data] pT balance in QCD di-jet events

Constraints the forward energy scale

60P. Loch


Expe

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uctio

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nd je

t phy

sics a

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LHC

(Par

t III)

Single Hadron Response

Determines basic response uncertainties at low energy Measure E/p for isolated tracks in collision events

500 MeV < p < 20 GeV Data/MC response agree very well

Larger discrepancies at higher momentum

61P. Loch


Expe

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(Par

t III)

In-Situ Calibration Validation

tot

prompt (direct) photon production:QCD Compton scattering

( 95% of )

annihilation

gq q

qq g

balance photon with (mostly) quarkjet pT to validate or constrain pT,reco,jet

Balancing jet pT with electromagnetic system Truth from collision

Based on idea that electromagnetic particles are well measured

Limits accuracy to precision of photon or electron signal reconstruction

Provides interaction (parton) level reference Note that simulation based approaches use

particle level reference Can use direct photon production

Kinematic reach for jet pT ~200-400 GeV for 1% precision – depends on center of mass energy

Relatively large cross-section Background from QCD di-jets – one jet

fluctuates into π0 faking photon Can also use Z+jet(s)

Cross-section suppressed, but less background – two electron final state cleaner

Can also use two muon final state

Note specific physics environment Underlying event different from other final

states Less radiation in photon/Z hemisphere

Often only good reference for quark jets Narrow jets in lower radiation environment

62P. Loch


Expe

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t phy

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t the

LHC

(Par

t III)

In-Situ Calibration Validation

Balancing jet pT with electromagnetic system Truth from collision

Based on idea that electromagnetic particles are well measured

Limits accuracy to precision of photon or electron signal reconstruction

Provides interaction (parton) level reference Note that simulation based approaches use

particle level reference Can use direct photon production

Kinematic reach for jet pT ~200-400 GeV for 1% precision – depends on center of mass energy

Relatively large cross-section Background from QCD di-jets – one jet

fluctuates into π0 faking photon Can also use Z+jet(s)

Cross-section suppressed, but less background – two electron final state cleaner

Can also use two muon final state

Note specific physics environment Underlying event different from other final

states Less radiation in photon/Z hemisphere

Often only good reference for quark jets Narrow jets in lower radiation environment

-boson + jet production:Z

balance Z pT reconstructed from decay leptons with quark jet pT to validate or constrain pT,reco,jet

63P. Loch


Expe

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(Par

t III)

Data Driven JES Corrections: Scale

Absolute response Goal:

Correct for energy (pT) dependent jet response

Tools: Direct photons, Z+jet(s),…

Measurement: pT balance of well calibrated

system (photon, Z) against jet in central region

Remarks: Usually uses central reference

and central jets (region of flat reponse)

Concerns: Limit in precision and estimates

for systematics w/o well understood simulations not clear

Needs corrections to undo out-of-cone etc. to compare to particle level calibrations

T

1

T,reco,jet T,absolute probe

T,

variation of jet response w

T,

T, T,reco,jetT,p

ith photon/

robe

T, reco,

ratio test variable ( , ):

withreference pT

average pT2

co h

( ) 1

s

Z p

Z

pp p

pp

E

pf

p

T,reco,jet T,reco,jet T, T,reco,jet T,

T, T, T,

jet

(relate to reconstructed jet variables with numerical inversion)

expected jet energy

relativecos ( ,

projection along reference pT)

:p p p p p

p p p

P

T,

T,absolute prob

2

e

correction from for well ca1 0

(

librated jets:

)

p

pf

P

P

64P. Loch


Expe

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(Par

t III)

Missing Transverse Energy Projections

Missing Transverse Energy Projection Fraction method (MPF)

Uses pT balance in photon+jet events to determine jet response

Technically on any jet response scale, but most useful if jet signal is corrected for e/h and other (local) detector effects

Based on projection of event missing transverse energy (MET) on photon pT direction

MET mostly generated by jet response Least sensitive to underlying event and pile-up due to

randomization in azimuth

Allows to validate the jet energy response Reference can be energy instead of pT

Basis of absolute jet energy scale in DZero Also under study for LHC

Considerations Perfect balance at parton level perturbed at particle level

Parton showering and hadronization, including initial and final state radiation (ISR & FSR)

Can be suppressed by selecting back-to-back photon-jet topologies

Imperfect calorimeter response generates missing transverse energy

Handle for calibration

T

T, T,jet T, T,jet

parton level part

T, T,jet

particle levelon level

T,

T, jet

balance in prompt photon production:

0

with calorimeter response and projection on :

(

0

p

p p E p

E

eE j

E

E

E

missT,jet T

missT

missT T, T,calo

all calo signal

missT, jet T,j

snot

et T

je

from

T

t

,caloall c

)

calculate from all calorimeter signals excludingthe photon sign

ˆ ˆ( )

( )

al:

E E

E

E j E E n n

E

E E

E

j

E

E

T,caloall caalo signals !

nlo signals

not from

T,

T, je

20 1 2

ot from

T,j

sc

t

ale

et

ˆ

ˆ

suppress biases by measuring response as function of yielding empiric

( ) ln ln

ally:

ˆ

cosh

E

E E

n

E n

j E b b bE

n

E

E E

rec,jet

scale

numerical inversionuse for !EE E

65P. Loch


Expe

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n a

nd je

t phy

sics a

t the

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(Par

t III)

Missing Transverse Energy Projections

Missing Transverse Energy Projection Fraction method (MPF)

Uses pT balance in photon+jet events to determine jet response

Technically on any jet response scale, but most useful if jet signal is corrected for e/h and other (local) detector effects

Based on projection of event missing transverse energy (MET) on photon pT direction

MET mostly generated by jet response Least sensitive to underlying event and pile-up due to

randomization in azimuth

Allows to validate the jet energy response Reference can be energy instead of pT

Basis of absolute jet energy scale in DZero Also under study for LHC

Considerations Perfect balance at parton level perturbed at particle level

Parton showering and hadronization, including initial and final state radiation (ISR & FSR)

Can be suppressed by selecting back-to-back photon-jet topologies

Imperfect calorimeter response generates missing transverse energy

Handle for calibration

ATL-PHYS-PUB-2009-015 (2009)

20 1 2

scale scale

( ) ln lnE Ej E b b bE E

ATLAS Simulations

66P. Loch


Expe

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(Par

t III)

JES In Situ Validations: Photon-Jet Balance

(from D. Schouten, In-situ measurements of Jet Energy Scale in ATLAS, talk given at “Workshop on Jet Measurements and Spectroscopy”, Pisa, Italy, April 18-19, 2011)

67P. Loch


Expe

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(Par

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W Mass Spectroscopy

In-situ calibration validation handle Precise reference in ttbar events

Hadronically decaying W-bosons Jet calibrations should reproduce W-

mass Note color singlet source No color connection to rest of collision

– different underlying event as QCD Also only light quark jet reference

Expected to be sensitive to jet algorithms Narrow jets perform better – as

expected

,recoSimulated di-jet invariant mass ( ) spectrum for jets with 0.4 (narrow jets)

in final states at 14 TeV

WMkT R

tt s

CERN-OPEN-2008-020arXiv:0901.0512 [hep-ex]

11 fb

68P. Loch


Expe

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(Par

t III)

JES From W Mass Reconstruction

W boson mass from two jets Clean event sample can be

accumulated quickly Original studies for center of

mass energy of 14 TeV and luminosity of 1033 cm-2s-1

~130 clean events/day in ttbar Angular and energy scale

component in reconstruction Energy scale dominant

,reco jet,1 jet,2 jet1,jet2

parton1,parton2jet1,jet2

jet1,jet2

invariant mass from decay jets:

bias from angular mismeasurement:

is small

2 1 cos

1 cos(cos

major contribution from energy s

) 11 cos

ca

WM E E

K

,PDG

jet,1 jet,1 jet,2 jet,2 jet1,jet2 jet1,jet2

jet,1 jet,1 jet,2 jet,2 jet1,jet2

jet,1 jet,2 ,reco

2 ( ) ( ) (cos ) 1 cos

2 ( ) ( ) 1 cos

( ) ( )

le:

simple rescaling method assuming energy independent sc

W

W

M

E E E E

E E E E

E E M

K

jet,1 jet,2( ) ( )ale shift works reasonably wellE E

jet1,jet2(cos )K

jet1,jet2cos

arXiv:0901.0512 [hep-ex]

69P. Loch


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(Par

t III)


jet (GeV)E

arXiv:0901.0512 [hep-ex]

,reco jet,1 jet,2 jet1,jet2

parton1,parton2jet1,jet2

jet1,jet2

invariant mass from decay jets:

bias from angular mismeasurement:

is small

2 1 cos

1 cos(cos

major contribution from energy s

) 11 cos

ca

WM E E

K

,PDG

jet,1 jet,1 jet,2 jet,2 jet1,jet2 jet1,jet2

jet,1 jet,1 jet,2 jet,2 jet1,jet2

jet,1 jet,2 ,reco

2 ( ) ( ) (cos ) 1 cos

2 ( ) ( ) 1 cos

( ) ( )

le:

simple rescaling method assuming energy independent sc

W

W

M

E E E E

E E E E

E E M

K

jet,1 jet,2( ) ( )ale shift works reasonably wellE E

W boson mass from two jets Clean event sample can be

accumulated quickly Original studies for center of

mass energy of 14 TeV and luminosity of 1033 cm-2s-1

~130 clean events/day in ttbar Angular and energy scale

component in reconstruction Energy scale dominant

70P. Loch


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(Par

t III)


2

JES scale relative to perfect jet response;resolution parameter relative to nominal jet energy resolution;find best matching template distribution ( )

for reconstructed distribution ( ):W

W

M

M

TR

T 2 2 2( ) ( )

1 1

stability of fit tested by subdividing total sample into 16

"measureme

( ) (

nts" (770 pb 1

)

6 48

min

pb ):

W WW W M M WM M dM

T RR

W mass from templates Produce W mass distribution templates

Use parton or particle level simulations

Smear with JES and resolution variations

Store W mass distributions as function of smearing parameters

Find response and resolution smearing parameters Find best fit template

( ), ( )W WM MT R

jet (GeV)E

arXiv:0901.0512 [hep-ex] arXiv:0901.0512 [hep-ex]

Measurement Number

71P. Loch


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(Par

t III)

Biases In W Mass Reconstruction

Boosted W pT boost reduces angle between decay jets

Reconstructed mass underestimates true W mass See example below for W boosted into the ATLAS end-cap calorimeter region

Pile-up can add energy to the system Not an improvement of the measurement – accidental and thus uncorrelated

jet energy shifts lead to shift in reconstructed mass

pile no

-up pile-up

included

34 2 110 cm s

14 TeVs

L

1.8W

,PDG

,reco

W

W

MM

P.Loch and P.Savard, in Proc. of the 7th Conference on Calorimetry in High Energy Physics, Tucson, Arizona,

1997 530-536, World Scientific (1998)

290 GeV

(GeV)WE

611 GeVT,Wp

72P. Loch


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JES In Situ Validations

Photon-jet pT balance Validate JES from MC

Balance jet pT with well measured photon pT

Compare data and MC predictions for central balance

Kinematical limitations 20 < pT(Jet) < 300 GeV at 2010 statistics

Multi-jet balance Validate leading jet pT in multi-jet final states

Balance leading jet pT (> 300 GeV) with several lower pT jets (recoil, individual jet pT < 300 GeV)

Assume that recoil system pT is validated by photon+jet/Z+jet

T,leading jet T,leading jet

T,recoilT,jet

other jets

p pMJB

pp

ATLAS-CONF-2011-029

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JES In Situ Validations

Photon-jet pT balance Validate JES from MC

Balance jet pT with well measured photon pT

Compare data and MC predictions for central balance

Kinematical limitations 20 < pT(Jet) < 300 GeV at 2010 statistics

Multi-jet balance Validate leading jet pT in multi-jet final states

Balance leading jet pT (> 300 GeV) with several lower pT jets (recoil, individual jet pT < 300 GeV)

Assume that recoil system pT is validated by photon+jet/Z+jet

T,leading jet T,leading jet

T,recoilT,jet

other jets

p pMJB

pp

74P. Loch


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Di-jet Balancing


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Di-jet Balancing


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Track Jet Reference

Ratio of calorimeter jet/matching track jet pT Surprisingly well understandable from simulations

Average behaviour well constrained in the presence of relative large fluctuations Not applicable jet-by-jet, requires significant statistics in each phase space bin considered

Covers pT transition between photon-jet and multi-jet pT balance within tracking acceptance ~200-~600 GeV jet pT

Sufficient overlap to avoid gaps in systematic error estimation Track jets also good reference to understand calorimeter response in presence of pile-up

Track vertex assignment allows id of tracks from primary collision Needs eta inter-calibration to extend to forward region

Larger errors expected – see before!

77P. Loch


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Use of Track Jets

T,calotrk

T,tracking

Datatrk

trk MCtrk

for matching jets

test double-ratio

pr

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Use of Track Jets

T,calotrk

T,tracking

Datatrk

trk MCtrk

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test double-ratio

pr

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Combining In-situ Validations

Requirements: Propagate all uncertainties in the data to the final uncertainty

Use realistic toy MC including interpolation between and averaging of contributing uncertainties

Minimize biases on the shapes of the measured distribution E.g, linear interpolation in pT in small steps (1 GeV)

Average data Consider all known correlations (e.g., from toy MC) and minimize error

spread as measured by the toy MC

HPVTools Software package performing above tasks

Originally from muon g-2 analysis

80P. Loch


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Combining In-situ Validations

Combined systematic uncertaintyfrom several in-situ techniques in ATLAS

Relative contribution from any given in-situ technique to the total systematic jet energy scale uncertainty in ATLAS

81P. Loch


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Different Final States: Quark Jets

tt qqb tt qqb

(high mulitplicity jets)SUSY

q


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ATLAS plots from arXiv:0901.0512 [hep-ex]

82P. Loch


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Different Final States: Quark Jets

tt qqb tt qqb


q


q

ATLAS plots from arXiv:0901.0512 [hep-ex]

No data - for demonstration only!

83P. Loch


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Other Sources Of JES Uncertainties

Longitudinal jet energy leakage Dangerous – can changes jet pT

cross-section shape at high pT Fake compositeness signal

Correlated with muon spectrometer hits Not strong correlation expected

Insufficient for precise JES Will likely not produce reconstructed

tracks, only

Helps to tag suspicious jets Suppress suspicious events/jets

Careful – real muon may be inside jet b decay Should produce track – cleaner signal

inside jet

Also background for missing transverse energy!

84P. Loch


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Calibration Refinement Using Jet Observables

85P. Loch


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Jet Calibration Refinement

Explicit use of jet shapes Global sequential calibration

Refined calibration applied on top of LC or other scale

E.g., in lieu of MC based numerical inversion techniques

Promising use of individual jet features Potential to improve jet-

by-jet energy measurement – jet energy resolution improvement

Works well with few basic jet variables Longitudinal energy

sharing Jet width in tracks or from

calorimeter

Energy fraction 1st layer Tile Energy fraction 3rd layer EM

Energy fraction PreSampler Calorimeter jet width

Basics for GS calibration: jet response variations as function of several sensitive variables

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Other Dedicated Uses Of Track Jets

Track jets Find jets in reconstructed tracks ~60% of jet pT, with RMS ~0.3 – not

a good kinematic estimator Dedicated 3-dim jet algorithm

Cluster track jets in pseudo-rapidity, azimuth, and delta(ZVertex)

Match track and calorimeter jet Helps response!

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pATLAS MC

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Jet Energy Resolution: Di-jet Balance

In-situ determination Di-jet balance

Soft radiation correction Extrapolate 3rd jet pT → 0

Bi-sector method Analyze fluctuations along bi-

sectors in transverse pane Suppress radiation

contribution

T T

T,1 T,2T,1 T ,2

T,1 T,2

2 2

T,1 T,2

T,1 T,2 TT

( , )

( )2

2

( )

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Ap

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p p

p p

p p pp

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Jet Energy Resolution: Bi-sector

In-situ determination Di-jet balance

Soft radiation correction Extrapolate 3rd jet pT → 0

Bi-sector method Analyze fluctuations along bi-

sectors in transverse pane Suppress radiation

contribution

ATLAS-CONF-2010-054

,calo radi

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,

2 2radi

ati

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n

,

o ,

most sensitive to calorimeter resolution effects:

, with

m

ost sensitive to (gluon) radiation effects:

assume radiation is rando

T

E

T

E

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k

2 2 2 2radiation, radiation, ,calo

m wrt jet directions:

E

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Jet Energy Resolution

Preliminary results Clear resolution improvement for

LCW, GCW, GS No conclusive performance

advantage for any of those Data compares to MC at ~10% level

Detailed look at performance gain Present baseline calibration EM+JES

not ideal –as expected Commissioning of LCW/GCW/GS

under way

Bi-sector method shown here – results from di-jet balance agree

with present errors of the methods

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Jets Not From Hard Scatter

Dangerous background for W+n jets cross-sections etc. Lowest pT jet of final state can be faked or

misinterpreted as coming from underlying event or multiple interactions

Extra jets from UE are hard to handle No real experimental indication of jet

source Some correlation with hard scattering? Jet area? No separate vertex

Jet-by-jet handle for multiple proton interactions Match tracks with vertices to calorimeter jet

Calculate track pT fraction from given vertex Classic indicator for multiple interactions is

number of reconstructed vertices in event Tevatron with RMS(z_vertex) ~ 30 cm LHC RMS(z_vertex) ~ 8 cm

If we can attach vertices to reconstructed jets, we can in principle identify jets not from hard scattering

Limited to pseudorapidities within 2.5!

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Particle Jets

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Transcript of Particle Jets