Transcript Document

Wolf G. Holzmann
23rd Winter Workshop In Nuclear Dynamics
Big Sky, Montana, February 11-17, 2007
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ATLAS HI Working Group
M. Baker, R. Debbe, A. Moraes, R. Nouicer, P. Steinberg, H. Takai, F. Videbaek, S. White
Brookhaven National Laboratory, USA
J. Dolejsi, M. Spousta
Charles University, Prague
A. Angerami, B. Cole, N. Grau, W. Holzmann, M. Lelchouk
Columbia Unversity, Nevis Laboratories,
USA
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L. Rosselet
University of Geneva, Switzerland
A. Denisov
IHEP, Russia
A. Olszewski, B. Toczek, A. Trzupek, B. Wosiek, K. Wozniak
IFJ PAN, Krakow, Poland
J. Hill, A. Lebedev, M. Rosati
Iowa State University, USA
V. Pozdnyakov
JINR, Dubna, Russia
S. Timoshenko
MePHI, Moscow, Russia
P. Chung, J. Jia, R. Lacey, N N.. Ajitanand
Chemistry Department, Stony Brook University, USA
G. Atoian, V. Issakov, H. Kasper, A. Poblaguev, M. Zeller
Yale University, USA
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Heavy Ion Physics at the LHC
Phase Diagram for Nuclear Matter
Pb+Pb collisions at the LHC
will produce partonic matter
at unprecedented T and 
Will allow for detailed study
and characterization of this
high energy density partonic
matter. Study evolution from
RHIC -> LHC energies.
ATLAS will target a comprehensive set of key
observables (see Nathan Grau’s ATLAS overview talk)
Here, I will exclusively focus on jet tomography.
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Jets as a tomographic probe of the medium
JetsJets
in h+h
in HI
collisions
collisions
Fragmentation:
Gyulassy et al., nucl-th/0302077
Rcone
R
cone
z
phadron
p parton
Jet modification sensitive to gluon densities, path length, ….
Jets as Tomographic Probes of the Medium!
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Jet tomography at RHIC
Jets studied statistically via singles yields and correlations…
interm. pT
correlations
high pT correlations
RAA
-h correlations
STAR, PRL 93 (2004) 252301
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Qualitatively successful, but quantitative interpretation difficult…
Jet tomography at RHIC
RAA not really constraining
T. Renk, hep-ph/0607166
E-loss models?
Correlation studies
complicated by trigger
bias effects?
-h correlations suffer
from statistics
Plus no real fragment. function measurements, etc…
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Jet tomography at LHC
How can jet studies at the LHC improve on the situation?
Truly high pT jets will be
produced copiously in
Pb+Pb collisions at the LHC
Can (and will) do RHIC type studies with better statistics
Can (and will) do high pT jet reconstruction
(event-by-event jet tomography, frag. functions, jet structure…)
Why would you want to do this with ATLAS?
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The ATLAS Calorimeter
EM
Barrel
ATLAS Calorimetery
Hadronic
Barrel
Forward
Finely segmented
calorimeter coverage
over full range
and large  range
EM
EndCap
Hadronic
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EndCap
Measuring Jets in The ATLAS Calorimeter
(Di)jets from PYTHIA
in Calorimter Towers
embedded in
HIJING event
Energetic jets clearly visible over the heavy ion background
Large  coverage is important
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Taking a closer look
 x  = 0.0028 x 0.1
Jet
Jet
All too wide
for single
photons
Back
ground
Background
– Segmentation of first EM sampling layer so fine that
heavy ion background is ~ negligible (unique at LHC)
– Fine  -> rejection of neutral hadron decays
– Clean 1st sampling-> prompt  isolation
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Two Approaches to Jet Reconstruction in ATLAS
A) Seeded Cone Algorithm
First approach:
use standard p+p cone algorithm
with background subtraction
Original cells
Cloned cells
Original towers
Layer-by-layer
subtraction
(exclude seeds)
Subtracted cells
Currently also looking
at methods to improve
algorithm: seed selection,
background subtraction, …
New towers
Reconstructed jets
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Jet Energy Resolution with Seeded Cone Algorithm
Study of different event samples embedded
into central Pb+Pb HIJING (b=0-2 fm)
Results obtained from standard p+p
cone algorithm w/ backgr.- subtraction
Some recalibration still needed.
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Can we control the flowing background?
Yes! Can measure dN/dϕ in different layers
(and sections) of calorimeters e.g. EM Barrel
η
η
Presampler
η
Layer 1
η
Layer 2
ϕ
ϕ
ϕ
ϕ
ϕ
ϕ
Layer 3
ϕ
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ϕ
Two Approaches to Jet Reconstruction in ATLAS
B) KT Algorithm clusters particles close in phase-space:
dij = min(k2ti,k2tj)R2 , where R2=(i-j)2+(i-j)2
diB = k2ti
Kt algorithm purposefully
mimics a walk backwards
along the fragmentation
chain
for all possible combinations: O(N3)
Cacciari et al: “Fast” Kt optimization to O(NlogN)
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How fast is fast?
M. Cacciari et al, hep-ph/0512210
“Fast” Kt algorithm outperforms cone algorithm,
Becomes feasible in heavy ion environment!
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“Fast” Kt Finder: Discriminating Jets and Background
Real Jets appear
as narrow towers
“Fake” Jets
appear flat and broad
Use jet topology to discriminate between jets and background!
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Discriminating Jets and Background: A First Look
E T,max = maximum ET in calo cell
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<E T > = average ET in calo cell
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2
2
Initial look seems promising.
Other variables can also be
constructed.
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3
4
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+Jet in ATLAS
PYTHIA  + jet (75 GeV) superimposed on b=4 fm HIJING
Pb+Pb event, full GEANT
Jet



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+Jet in ATLAS
PYTHIA  + jet (75 GeV) superimposed on b=4 fm HIJING
Pb+Pb event, full GEANT
Background subtracted
Jet



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EM Layer 1 ET (GeV)
+Jet in ATLAS
Δη×Δϕ = 0.003x0.1

One (of 64) rows
in barrel EM
calorimeter 1st
sampling layer

Isolated photon gives clean
signal in EM first sampling
layer
Even in central Pb+Pb !
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+Jet in ATLAS
Direct  triggered angular
correlations
energy calibrated:
- jet studies
- mach cone studies
Photon bremsstrahlung in
jet cone?
Many interesting possibilities:
let your imagination run wild :-)
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Summary and Outlook
Jet modification studies at the LHC hold much potential for
quantitative tomography of the partonic medium
Lots of ground work on jet reconstruction in heavy ion environment
(seeded cone algorithm, fast Kt algorithm, different background
subtraction schemes, etc…) being done in ATLAS
Studies shown only an “amuse gueule” expect much more, soon
ATLAS is uniquely positioned to perform key jet measurements well
New collaborators are welcome!
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Backup Slides
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Jet Position Resolution with Seeded Cone Algorithm
Resolutions in  and  for <ET>~50 GeV
Results obtained from standard p+p
cone algorithm w/ backgr.- subtraction
Some recalibration still needed.
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The ATLAS Calorimeter
 x  = 0.0028 x 0.1
Jet
Jet
All too wide
for single
photons
Back
ground
Background
– Segmentation of first EM sampling layer so fine that
heavy ion background is ~ negligible
– Fine  -> rejection of neutral hadron decays
– Clean 1st sampling-> prompt  isolation
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The ATLAS Calorimeter
Δη×Δϕ in LAr Barrel:
Layer 1: 0.003x0.1
Layer 2: 0.025x0.025
Layer 3: 0.05x0.025
Finely segmented calorimeter coverage over full range
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and large  range
Advantages of “Fast” Kt Algorithm
Infrared and collinear safe
Exceptionally suited to study jet sub-structure:
- modification of jet topology in Pb+Pb
- hard radiation within the jet
New ways to distinguish jets and background
Systematic cross-check to cone algorithm
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