Martin Spousta for the ATLAS Collaboration

22.6.2010

Jet results and jet reconstruction

techniques in pp and their prospects in

heavy-ion collisions in ATLASMartin Spousta

for the ATLAS Collaboration

Martin Spousta, Charles University in Prague 2

22.6.2010

Outline

• Motivation• Choice of the jet algorithm• Measurement of jet energy spectra in heavy ions

– jet energy scale– jet energy resolution

• Measurement of jet internal structure in heavy ions– jet shapes– fragmentation function and jT distribution– complementary quantities to study the energy loss

• Recent results from jet measurement in pp collisions at 7 TeV– inclusive jet cross section– jet shapes

• Summary


22.6.2010

Motivation: Why ATLAS?

tracking over 5 units of pseudorapidity –

fragmentation studies

jet reconstruction using calorimeter,full azimuth, 10 units of pseudorapidity

first layer of LAr EM calorimeter excellent for photon isolation,

other layers also well segmented

ZDC to estimate centrality and reaction plane and to

measure UPC


22.6.2010

Motivation: Expected jet rates

• Assuming 2 weeks of HI running means 1/20 of nominal luminosity (∫L = 25 b-1)

• Full ATLAS acceptance, 100% trigger efficiency

• Reasonable rates

– 4000 jets with ET> 100 GeV

– 50 jets with ET > 200 GeV

by W. Vogelsang

• Pb+Pb (<Ncoll> = 440)

• √sNN=2.75 TeV

• jets with R=0.4

• ∫L = 25 μb-1 (one year)

• σPb+Pb/σp+p=100

• full acceptance |η|<4.5


22.6.2010

Jet reconstruction strategy

Background / underlying event subtraction

First subtract the background,then reconstruct jets

Cone-type

Jet finding algorithm

Cluster-typekT algorithm

(k=1)

anti-kT algorithm

(k=-1)

Seeded iterative cone algorithm

Infrared & collinear safe SISCone

algorithm

First reconstruct jets,then separate signal jets from background and subtract the background

Cambridge/Aachen(k=0)


22.6.2010

Comparison among jet finding algorithms

• Which jet algorithm we should use? How the UE influences the jet finding?

• kT algorithm exhibits serious problems with the jet energy scale: for more severe background, kT underestimates the jet area and thus the energy. This is due to the fact that kT preferably clusters the softer part of a jet with the background

• Problematic algorithms: - kT algorithm, - Cambridge/Aachen

• Non-problematic: - anti-kT algorithm, - cone algorithm

mean bkgr Et

size of the fluctuations

in bkgr

jet energy scale

20% underestimation

Toy model: Poisson-like background (“UE”) randomly generated to allow variation of mean and rms of ET

kT algorithm


22.6.2010


• Which jet algorithm we should use? How the UE influences the jet finding?

• kT algorithm exhibits serious problems with the jet energy scale: for more severe background, kT underestimates the jet area and thus the energy. This is due to the fact that kT preferably clusters the softer part of a jet with the background

• Problematic algorithms: - kT algorithm, - Cambridge/Aachen

• Non-problematic: - anti-kT algorithm, - cone algorithm

mean bkgr Et

size of the fluctuations

in bkgr

jet energy scale

no problems with jet energy scale

anti-kT algorithm

Toy model: Poisson-like background (“UE”) randomly generated to allow variation of mean and rms of ET


22.6.2010


Area of jets delivered by different algorithms is very different in the noisy environment

• kT algorithm, R=0.4

• smaller jets with irregular areas• denser UE leads to smaller jet areas

• anti-kT algorithm, R=0.4

• conical jets with the average radius of R

the sameevent

Jet

ID n

um

ber

Jet

ID n

um

ber

jet ET

(GeV)


22.6.2010

Characteristic properties of anti-kT algorithm

Mach cone? No. This is just an effect of optimization of jet axis with respect to the jet periphery ... characteristic for the anti-kT algorithm.

One needs to be careful about systematic biases coming from usage of a given jet algorithm.

Let’s look at multiplicity of particles as a function of the distance from the jet axis (charged PYTHIA particles, MinBias pp collisions, √s = 900 GeV)

We see a“Mach-cone”!


22.6.2010

truth jet ET (GeV)

Jet energy resolution

p+p Pb+Pbb=2fm, dN/d~2700

Fitting well known formula: to p+p and Pb+Pb energy resolution

• Jet energy resolution below 25% for 70 GeV jets in the most central collisions (dN/d~2700 b=2 fm, unquenched HIJING, cone algorithm)

• Irreducible background fluctuations from HIJING simulations: ~ 15 GeV / jet ... ... but reality can be very different ...

stochastic term noise term

||<5.0

ATLAS PreliminaryATLAS Preliminaryconstant term

truth jet ET (GeV)


22.6.2010

Jet energy resolution

... fluctuations in the underlying event are highly model dependent!

Once we know real fluctuations we can easily compute limiting jet energy resolution in PbPb collisions!

=Realistic jet energy resolution

in PbPb

truth jet ET (GeV) calorimeter tower ET (MeV)

<ET>=1.8 GeV, (ET)=0.8 GeV<ET>=1.3 GeV, (ET)=0.6 GeV

— HIJING— HYDJETb = (0-3) fm, ||<5

(ET)=0.8 GeV means p1~15 GeV

(ET)=0.6 GeV means p1~11 GeV!

Real heavy ion underlying event

||<5.0

p+p

ATLAS Preliminary

, PYTHIA


22.6.2010

Fragmentation function and jT

distribution:unquenched jets

Reconstruction procedure:• tracks are matched to calorimeter towers of a jet• jT and z for tracks with pT>2 GeV is computed• background distributions of jT and z are computed using

tracks that match with HIJING particles, these distributions are subtracted, correction for the jet position resolution is applied

… we can well reproduce jT distribution and fragmentation functionin the presence of heavy-ion UE

z

jT

ATLAS Preliminary

○ - truth● - reconstructed▼ - background(jet ET 70-140 GeV)

Simulation of central PbPb collisions

ATLAS PreliminarySimulation of central PbPb collisions○ - truth● - reconstructed▼ - background(jet ET 70-140 GeV)


22.6.2010


distribution:

quenched jets

Large jT suppressed gluons radiated from large angles

Low z enhanced, higher z suppressed leading particle suppressed, redistribution of

energy out of a jet core

Effect of jet quenching simulated by PYQUEN (without heavy ion UE) ...... if the quenching is of the order of PYQUEN prediction we should be

able to measure it

Reconstructed○ - unquenched● - quenched(jet ET=70-140 GeV)

ATLAS Preliminary

Reconstructed○ - unquenched● - quenched(jet ET=70-140 GeV)

ATLAS Preliminary


22.6.2010

• black = reference, computed using simulation of pp jets, <ET>=60 GeV

• open = smearing of the jet axis by convoluting with gaussian (R)=0.06 - leads to: characteristic shift in jT distribution; fragmentation function remains almost unaffected

• blue = simulation of jet energy scale shift by +10% - leads to:characteristic shift in fragmentation function; jT distribution remains unaffected

UE or detector effects could mimic the jet quenching – careful cross-checks are needed.

Simulation of pp jets(jet ET 70-140 GeV)

ATLAS Preliminary


ATLAS Preliminary


distribution: detector / UE effects


22.6.2010

Jet shapes

• Measure the energy flow inside the jet at the calorimeter level

• First jet shapes already measured in 7 TeV pp collisions at ATLAS! More details later

jet axis

... integral jet shape

... differential jet shape


22.6.2010

• black = reference jet shape computed using simulated pp jets

• red = adding gaussian noise with <ET>=0, (ET)=1.4 GeV leads to:

– jet energy resolution deterioration, (Et/Et) = 20%

– jet position resolution deterioration (R) = 0.06

– characteristic change in the jet shape, and pT spectra

• green = we can recover the jet shape e.g. by re-computing the jet axis using simple cone algorithm with R randomly chosen in <0.1,0.5>

Jet shape measurements: UE / detector effects

jet shape jet energy flowSimulation of pp jets(jet ET 70-140 GeV)


ATLAS Preliminary ATLAS Preliminary


22.6.2010

Effect of the calorimeter can be corrected by unfolding (bin-by-bin as good as bayesian unfolding)

Jet shape measurements: UE / detector effects

○ - particle level● - detector level

○ - particle level● - detector level□□ - bayesian□□ - bin-by-bin

Simulation of pp jetsjet ET 70-140 GeV

Simulation of pp jetsjet ET 70-140 GeV



22.6.2010

Modification of jet shapes

• Jet quenching effect predicted by PYQUEN is well measurable

PbPb – no quenching(jet ET = 70-140 GeV)

(jet ET = 70-140 GeV)


• Jet shape can be well measured – it is almost centrality independent (after applying corrections on jet position resolution)


22.6.2010

jet shape

jet shape

jet energy spectrum

jet energy spectrum

PYQUEN with only radiative

energy loss

PYQUEN with only collisional

energy loss

○ - non-quenched● - quenched

○ - non-quenched● - quenched - non-quenched

- quenched

- non-quenched - quenched

Complementary quantities to measure different energy loss

mechanisms

We need to use different quantities to learn about energy loss mechanism




22.6.2010

Inclusive jet cross section measured

in pp collisions at 7 TeV• Inclusive jet differential cross section

measured in pp collisions at 7 TeV using L ~ 17 nb-1 (uncertainty of 11%).

• Jets defined using R=0.6 anti-kT algorithm. Topological calorimeter clusters used in the input for the jet reconstruction.

• Data corrected for detector effects using bin-by-bin correction and compared to NLOJET++ with CTEQ6.6 PDFs.

• Theory uncertainties due to the choice of PDFs, renormalization and factorization scheme, s, and non-perturbative effects estimated and summed in quadrature (orange band)

• jet energy scale < 9%

• perturbative effects estimated and summed in quadrature (orange band).

• Main contribution to the systematic errors (grey band) is due to the jet energy scale uncertainty which is below 9% in the entire pT and rapidity range (below 7% for the central jets with ||<0.8).


22.6.2010

Inclusive jet cross section measured

in pp collisions at 7 TeV

• Double differential single inclusive jet cross section for different rapidity bins as a function of pT (left) and for different pT bins as a function of rapidity (right).

• Data matches theory within systematic and statistical errors in all pT and rapidity bins.


22.6.2010

Jet shapes in pp collisions at 7 TeV

• Jet shapes measured at the calorimeter level (left) and using charged tracks (right).

• Differential jet shape measured using anti-kT jets with R=0.6. Data compared with detector level MC simulation.

• Data agrees with Monte Carlo up to 10% in the whole pT,jet range of 40 – 210 GeV and rapidity range of |y| < 2.8.

• ATLAS tune of Herwig + Jimmy provide the best description of the jet shapes.


22.6.2010

Summary

• ATLAS detector is well suited for jet studies in heavy-ion collisions (it is also well suited for soft heavy ion physics, quarkonia, ultra-peripheral collisions, and other important measurements).

• We have stressed some caveats of the measurement of jets and their properties that can influence our understanding of the jet energy loss mechanism.

• We have shown first results on inclusive single jet cross section measurement and jet shape measurement in pp collisions at √sNN = 7 TeV.


22.6.2010

Backup

Backup


22.6.2010

Jet reconstruction strategy

Cone jet reconstruction:

• regions of interest found (seed regions) – fast sliding window algorithm used (size of the sliding window 0.3x0.3)

• background computed • excluding the seed-regions

(R=0.8 outside of a seed)• vs. (binning of 0.1) • vs. layer (24 calorimeter layers)

• background subtracted

• standard p+p jet finding algorithm used (seeded iterative R=0.4 cone algorithm)

An alternative: (anti)kT-algorithm based reconstruction strategy

PYTHIA dijets embedded into the unquenched HIJING events

one event before the background subtraction

one event after the background subtraction


22.6.2010

Fast (anti)kT jet reconstruction strategy

(anti)kT jet reconstruction:

• run fast (anti)kT algorithm• separate “signal” jets from the

“background” jets • use regions outside of signal jets to

estimate the background• subtract the background from jets ... motivated by Cacciari and Salam

An alternative: Use the same procedure as for the cone algorithm (previous slide) but subtract the background after the jet finding.


22.6.2010

Jet energy scale and efficiency

dN/d~2700

• Jet energy scale within 5% above 60 GeV in the most central collisions simulated by “unquenched” HIJING (dN/d~2700 b=2 fm). Cone algorithm R=0.4.

• Below 60 GeV the efficiency is steeply decreasing jet energy scale shift (it is more probable to find jets with higher energy)

To trust any jet observation we need to have efficiency under the control.

PbPbb=2 fm, dN/d~2700



22.6.2010

Jet position resolution

• Jet position resolution in similar to that in (full field simulated)

• It improves with increasing jet energy, in the whole energy range jet position resolution is better than half of the tower size

• Jet position resolution can be improved using smaller cones: jet axis of a reconstructed jet is substituted by the jet axis from jet reconstructed with R<Rorig

Pb+PbdN/d~2700

Pb+PbdN/d~2700

Towersize


Cone algorithm Cone algorithm


22.6.2010

Efficiency and fake-rate

• Efficiency is almost centrality independent – easier interpretation of jet properties vs. centrality

• Above 70 GeV the efficiency is above 90%

• Above 70 GeV very low fake rate < 5% (without any fake rejection)

ATLAS Preliminary

ATLAS Preliminary



22.6.2010

• associated and leading jets with ET>100 GeV

Dijet correlations




22.6.2010

Tracking – resolution, efficiency

70%

ATLAS Preliminary

ATLAS Preliminary

ATLAS Preliminary

ATLAS Preliminary


22.6.2010Backup slides

Fake jet reduction

ATLAS Preliminary

ATLAS Preliminary


22.6.2010

Statistics

only the most central collisions

x jet quenching effect simulated with 5000 PYQUEN events


22.6.2010

pp vs PbPb – important parameters

Documents

Martin Spousta for the ATLAS Collaboration