Transcript From yesterday Jet I: Intro & Motivations Jet II: Full Jet Reconstruction Elena Bruna (Yale&INFN Torino) Why jets in heavy ion collisions? Jet Tomography! • Jet quenching.

From yesterday
Jet I:
Intro & Motivations
Jet II:
Full Jet Reconstruction
Elena Bruna (Yale&INFN Torino)
Why jets in heavy ion collisions? Jet Tomography!
• Jet quenching observed at RHIC & LHC via single
high-pT hadron and di-hadrons
• Access kinematics of the binary hard-scattering
• Characterize the parton energy loss in the hot QCD
medium
• Study medium response to parton energy loss
• Jet-finding connects Theory and Experiment
• Many Jet Finders on the market
 need to be collinear/infrared safe
• Choice of R matters
Today and tomorrow
Jet II:
Full Jet Reconstruction
• Jet-finding connects Theory and Experiment
• Many Jet Finders on the market
 need to be collinear/infrared safe
• Choice of R matters
Goal: set the Jet Energy Scale
• Different systematics to take into account
(tracking,…)
• Background fluctuations: the challenge
Jet III:
Results
Jet IV:
The Present: from RHIC to LHC
Elena Bruna (Yale&INFN Torino)
Jets in Heavy-Ion Collisions at
RHIC and LHC
Central Au+Au √sNN=200 GeV
STAR EMC + tracking data
ETjet ~ 21 GeV
Central Pb+Pb√sNN=2.76 TeV
ALICE tracking data
STAR preliminary
Why measure jets in heavy ion collisions? [inclusive, di-jets, jet-hadron, g-jet,..]
• Access kinematics of the binary hard-scattering
• Characterize the parton energy loss in the hot QCD medium
− modified fragmentation, energy flow within jets, quark vs gluon jet difference
− flavor and mass dependence
• Study medium response to parton energy loss – establish properties of the medium
Elena Bruna (Yale&INFN Torino)
3
Jets in p+p
ATLAS Nov/Dec 2009
√s=2.36 TeV
Tevatron: CDF @ √s = 1.8 TeV
RHIC p+p @ √s = 200 GeV
p+p JP trigger ~ 21 GeV
pt per grid cell [GeV]
STAR Preliminary
Jet
p+p
η
Elena Bruna (Yale&INFN Torino)
ϕ
p+p:
the reference measurement
 need well calibrated probes
4
Jet-finding and systematics..
Hadronic and electron double counting
Electrons and hadrons can deposit
energy in the EMC and leave tracks in
the TPC.
Be aware of double counting!
Avoid double counting (p+p and A+A):
• remove EMC towers that match to an
electron
• remove fraction f of tower energy for
tower that match to hadrons
corr
rec
E cluster
 E cluster

N matched
f p
i
h
i 0
choice of f to be determined by experiment. f=100%, MIP,…
Elena Bruna (Yale&INFN Torino)
5
Jet Energy resolution – the Jet
Energy Scale
PYTHIA:
p+p √s=200 GeV
X axis=Pythia jet pT
(Particle Level)
Y axis=Pythia thru STAR
detector simulation
(Detector Level)
(1) Reconstructed Jet pT on average smaller than the Input (PYTHIA) jet pT
(2) The reconstructed jet pT is smeared
Need to know (1) and (2) to correct the measured jet pT back to the “true” jet pT
Can use PYTHIA to determine the jet energy resolution
Elena Bruna (Yale&INFN Torino)
6
Jet-finding and systematics..
Tracking efficiency
[Remark: PYTHIA is OK for p+p. Data-driven correction scheme preferred for A+A]
Charged jet component has to be corrected for the pT dependent tracking efficiency:
ε(pT)= tracking efficiency
pT,hi= single track transverse
momentum
N = # of jet constituents
N
eff 
p
i
T ,h 
i 0
N
p
i
T ,h 
i 0
1/( pT )
Simulation:
p+p @ 5.5 TeV in ALICE
Other systematics:
tracking performance at high-pT, high-luminosity track distortion,
unobserved neutral energy, … [see backup slides]
Elena Bruna (Yale&INFN Torino)
7
Jets in A+A
Goal
Reconstruct the full jet kinematics
of hard scattering in unbiased way,
even in presence of (underlying)
heavy-ion collision.
~ 21 GeV
pt per grid cell [GeV]
STAR preliminary
di-jet event
η
Elena Bruna (Yale&INFN Torino)
ϕ
8
Background in A+A
~ 21 GeV
rA [Gev]
pt per grid cell [GeV]
STAR preliminary
di-jet event
η
ϕ
Out-of-cone
area
Au+Au 0-20%
Rc=0.4, no pt cut, out-of-cone area
STAR Preliminary
STAR preliminary
Reference multiplicity (~centrality)
pT (Jet Measured) ~ pT (Jet) + ρA ± STAR
F Preliminary
Three main components:
1. Background energy in R=0.4 ~ 45 GeV at RHIC, ~90 GeV at LHC
2. Substantial region-to-region background fluctuations described by F
3. “Fake” jets: random association of uncorrelated soft particles (i.e. not due to hard
scattering)
Elena Bruna (Yale&INFN Torino)
9
Background in A+A
pT (Jet Measured) ~ pT (Jet) + ρA ± F
• reconstruct event with kT (jets+bkg)
• all jets in acceptance {pT,i}
• A = jet area in η-ϕ
ρA [Gev]
Event-wise background estimate:
Au+Au 0-20%
Rc=0.4
STAR preliminary
STAR Preliminary
Reference multiplicity (~centrality)
Why the median?
The background estimate has to be independent of the ‘true signal jets’.
The ‘true jets’ do not pull the median compared to the mean.
Elena Bruna (Yale&INFN Torino)
10
Background in A+A
pT (Jet Measured) ~ pT (Jet) + ρA ± F
~ 21 GeV
pt per grid cell [GeV]
STAR preliminary
In a given Area, the background is
subject to fluctuations around the
median ρ
di-jet event
η
Elena Bruna (Yale&INFN Torino)
ϕ
How to quantify fluctuations and
fake jets?
STAR Preliminary
11
Background in A+A
pT (Jet Measured) ~ pT (Jet) + ρA ± F
pt per grid cell [GeV]
STAR preliminary
di-jet event
η
In a given Area, the background is
subject to fluctuations around the
median ρ
How to quantify fluctuations and
fake jets?
STAR Preliminary
• Embed a probe ϕ
particle in the event: pTemb
• Reconstruct hybrid event with anti-kT
• Match reconstructed jet with embedded probe in (h,f):
pTcluster, Acluster
Quantify response via:
Elena Bruna (Yale&INFN Torino)
12
Assessing background fluctuations
f (pT,clus Meas – ρA – pTemb)
Example: pT,clus for only background clusters (no true jet)
pTemb=0
No fluctuations
Gaussian fluctuations
R=0.2
σ=3.5 GeV
pT,clus Meas – ρA
pT,clus Meas – ρA
“Thermal” fluctuations
Akt, R=0.2
Simulation
T=290 MeV
dN/dη=650
pT,clus Meas – ρA
How to characterize the full shape of the bkg fluctuations?
Elena Bruna (Yale&INFN Torino)
13
Background fluctuations: δpT
Single particle embedding
in real Au+Au
pT=30 GeV
=-0.2
Gaussian fit to left-hand side (LHS):
• LHS: good representation
• RHS: non-Gaussian tail (real jets are there!)
• centroid non-zero(~ ±1 GeV)
 contribution to jet energy scale uncertainty
Elena Bruna (Yale&INFN Torino)
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Background fluctuations: δpT
Simple model: uncorrelated particle emission
FM (A)
• M(A) = particle multiplicity in area A 
• <pT> = mean pT in a given area A

Poisson
F p T  (A) Gamma
M. Tannenbaum
Background fluctuation distribution in a given area A in (η,ϕ):
Phys. Lett. B498 (2001) 29
F( pT ;A)
 FM (A)  F pT  (A)
• No hard scattering
• No correlations
• Two parameters


Simple uncorrelated-emission
model accounts for the bulk of
background fluctuations (!)
Elena Bruna (Yale&INFN Torino)
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Background fluctuations: δpT
Systematics
So far, embedded single particles
But jet ≠ single particles
investigate dependence on
fragmentation patterns:
PYTHIA, QPYTHIA
δpT insensitive to different fragmentation
Crucial for quenched jets, whose fragmentation is unknown!
What do we do once we know the dpT shape?
Elena Bruna (Yale&INFN Torino)
arXiv:1012.2406
16
Unfolding the underlying event
• Jet Energy resolution distorts measured jet cross section
• Background distorts measured jet cross section
• Unfolding technique used to extract the ‘true’ jet spectrum  jet energy scale
Pythia
Pythia
smeared
Pythia
unfolded
unfolding
Elena Bruna (Yale&INFN Torino)
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Unfolding/deconvoluting/unsmearin
g
• Given true measure mj (i.e. true jet pT) and response function Rij
(inefficiencies, irresolutions, …) the experiment will measure:
n i  Rijm j
• Ideally (i.e. with infinite statistics) we can determine mj from ni by inverting
Rij
m  R1n

(m)
 stats. so
• Don’t have infinite
need to solve for miteratively.
Example of response matrix used for
unfolding the underlying event.
[dpT=Gaussian, s=6.5 GeV]
- RooUnfold:
http://hepunx.rl.ac.uk/~adye/software/unfold/RooUnfold.htm
l
- 5 methods: D. D’Agostini, NIM.A362:487 (2005), …
(n)
Elena Bruna (Yale&INFN Torino)
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Jet III: Results
Elena Bruna (Yale&INFN Torino)
Jets in p+p: calibrated probes?
√s=2.36 TeV
Tevatron: CDF @ √s = 1.8 TeV
RHIC p+p @ √s = 200 GeV
p+p JP trigger ~ 21 GeV
pt per grid cell [GeV]
STAR Preliminary
Jet
ATLAS Nov/Dec 2009
p+p
η
Elena Bruna (Yale&INFN Torino)
ϕ
p+p:
the reference measurement
 need well calibrated probes
20
Jets are calibrated probes
RHIC
Tevatron
Jet cross section in p+p (STAR), p+p, DIS, well described by pQCD
Jets in p+p are a good reference for A+A
Elena Bruna (Yale&INFN Torino)
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Fragmentation Functions in p+p
Preliminary
20 <Jet pTreco< 30 GeV/c
Data not
corrected
to particle
level.
R=0.4
“PYTHIA”
= PYTHIA
+GEANT
Preliminary
Preliminary
30 <Jet pTreco< 40 GeV/c
Preliminary
Reasonable agreement between data and PYTHIA
Jets in p+p are a good reference for A+A
Elena Bruna (Yale&INFN Torino)
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The underlying event in p+p
| Angle relative to leading jet
•“Toward” Δφ < 60o
•“Away” Δφ > 120o
•“Transverse” 60o < |Δφ| < 120o
• TransMax - Trans. region
with highest p or N
• TransMin Trans. region with
T
track
least pT or Ntrack
Underlying event = what is contained in the Transverse region, i.e.
everything BUT the hard scattering
Elena Bruna (Yale&INFN Torino)
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The underlying event in p+p
Agreement between PYTHIA and data
Underlying event is decoupled from the hard scattering
Elena Bruna (Yale&INFN Torino)
24
The underlying event in p+p
Agreement between PYTHIA and data
Underlying event is decoupled from the hard scattering
Elena Bruna (Yale&INFN Torino)
25
Jets in d+Au: why?
Control experiment:
Measure possible initial state/Cold Nuclear Matter (CNM) effects
Probe the “cold medium” via d+Au collisions (compare to p+p)
Jet
d+Au
Elena Bruna (Yale&INFN Torino)
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Jets in d+Au
σkT,raw (p+p) = 2.8 ± 0.1 GeV/c
σkT,raw (d+Au) = 3.0 ± 0.1 GeV/c
No strong Cold Nuclear Matter effect on jet kT broadening
seen
Systematics under investigation
Elena Bruna (Yale&INFN Torino)
27
Jets in d+Au
No significant deviation from Nbin scaling in d+Au
Initial state/Cold nuclear matter effects in the kinematic
range as measured in d+Au seem to be small
Systematics under investigation
Elena Bruna (Yale&INFN Torino)
28
So far, so good
Jet
p+p
Jet
d+Au
Elena Bruna (Yale&INFN Torino)
p+p:
the reference measurement
 calibrated probes !
✓
d+Au:
the control measurement
 No strong Cold Nuclear
Matter effect
✓
29
Jets in Au+Au: what to expect?
- for unbiased jet reconstruction -
Jet energy fully recovered even in case of quenching
Jet is a hard process, scales as Nbin
Inclusive spectra:
jet
s
• R jet 
1
AA
AA
N bin s ppjet
Di-jet analyses:
• Ratio of recoil spectra Au+Au/p+p = 1
• Modified fragmentation in case of dense medium
Elena Bruna (Yale&INFN Torino)
30
Inclusive measurements
• Inclusive Jet spectrum measured in central Au+Au collisions at RHIC
• Extended the kinematical reach to study jet quenching phenomena to
jet energies > 40 GeV
Elena Bruna (Yale&INFN Torino)
31
Inclusive measurements
• Inclusive Jet spectrum measured in central Cu+Cu collisions at RHIC
• Extended the kinematical reach to study jet quenching phenomena to
jet energies > 40 GeV
Elena Bruna (Yale&INFN Torino)
32
Jet RAA
jet
s
jet
RAA

1
jet
N bin s pp
AA
Inclusive RAA
• RAAjet<1 We see a substantial fraction of jets
- in contrast to x5 suppression for light hadron RAA (RAAjet > RAA )
• kT and Anti-kT known to have different sensitivities to background
Elena Bruna (Yale&INFN Torino)
33
Jet energy profile: first look
Jet inclusive measurements: 0.2 vs 0.4
R=0.4
R=0.2
p+p:
•
•
jets more collimated with increasing pT
PYTHIA (fragmentation +
hadronization) describes the data
Solid lines:
Pythia – particle level
Elena Bruna (Yale&INFN Torino)
34
Jet energy profile: first look
Jet inclusive measurements: 0.2 vs 0.4
G. Soyez – priv. comm. 2010
Solid lines:
Pythia – particle level
NLO ≈ PYTHIA parton level
PYTHIA hadron level ≈
HERWIG hadron level
Be careful when comparing to theory: Hadronization broadens the jet
Elena Bruna (Yale&INFN Torino)
35
Jet energy profile: first look
Jet inclusive measurements: 0.2 vs 0.4
R=0.4
R=0.2
p+p:
•
•
jets more collimated with increasing pT
PYTHIA (fragmentation +
hadronization) describes the data
Au+Au:
•
ratio lower than p+p
• “Deficit” of jet energy for jets
reconstructed with R=0.2
Elena Bruna (Yale&INFN Torino)
36
Jet energy profile: first look
Jet inclusive measurements: 0.2 vs 0.4
R=0.4
R=0.2
Red: p+p
Blue: Au+Au
Suggests strong broadening of the energy profile
Elena Bruna (Yale&INFN Torino)
37
Jets in p+p and Cu+Cu in
PHENIX
PHENIX uses a Gaussian filter approach
Cone-like, but no fixed angular cut-offs
Implements fake jet rejection
Elena Bruna (Yale&INFN Torino)
38
Jets in A+A: possible biases
CAVEAT:
jet-finder based on unmodified
jet-shapes
⇒ veto against
modified/quenched jets
“Anti-quenching” biases!
pT cut to minimize background
⇒ bias towards less-interacting
jets
Can we exploit the biases?
Elena Bruna (Yale&INFN Torino)
39
Di-jet measurements
Trigger jets are biased towards the surface.
Recoil jets are exposed to a maximum pathlength in the medium.
Large energy loss expected.
Anti-kT, R=0.4
Trigger Jet: pT,cut=2 GeV/c, pT(trig)>20 GeV/c
Coincidence rate:
how often I measure a recoil
jet once the trigger jet is found
σ=6.5 GeV/c
Elena Bruna (Yale&INFN Torino)
40
BACKUP
Elena Bruna (Yale&INFN Torino)
Jet Energy resolution with di-jets
Particle-Detector jet Res:
pTJet(Part.Lev) – pTJet(Det.Lev)
~10-25 %
di-jet Res:
pTJet 1– pTJet 2
(PY Det. Lev.) ~
good!
(dijet data) :
But:
(dijet PY Det. Lev.) >
(Part-Det)
di-jet imbalance includes both energy
resolution and kT (initial state) effect!
[kT=pTjet sinDfdijet]
kT: good agreement between data and
simulation
Use PYTHIA to determine the jet energy resolution
Elena Bruna (Yale&INFN Torino)
42
Jet-finding and systematics..
Tracking performance
Tracking is limited by misalignment, luminosity, resolution…
• Rare processes as high-pT jets are likely to come from high luminosity runs
Example of high-luminosity distortion? Space-charge effect  accumulation of
space charge in the TPC that causes an anomalous transport of drifting electrons in
the TPC, affecting the tracking performance by shifting the momentum up or down
(depending on the charge)
•Tracking resolution at high-pT is
expected to deteriorate  need to apply
an upper pT cut on tracks
PYTHIA simulation: p+p 200 GeV
effect of upper pT cut on jet energy scale
Elena Bruna (Yale&INFN Torino)
43
Jet-finding and systematics..
Unobserved neutral energy
Experiments like STAR and ALICE do not detect neutral, long-lived particles
(neutrons, K0L)
PYTHIA simulation:
p+p at 200 GeV
• mean missed E ~ 9%
• median missed E <0.3 %
• 50% of jets loose no energy
• model dependent
Elena Bruna (Yale&INFN Torino)
44
Fragmentation Functions
large uncertainties due to background
(further systematic evaluation needed)
AuAu (Jet+Bkg)
AuAu (Bkg)
STAR preliminary
Jet energy determined in R=0.4
pT Jet(trig)>20 GeV
pTcut=2 GeV
Charged particle FF: R(FF)=0.7
xrec=ln( pT,Jet rec / pT,hadr)
AuAu: FF(Jet)=FF(Jet+Bkg)-FF(bkg)
Bkg estimated from charged particle spectra out of jet cones
Bkg dominates at low pT
Elena Bruna (Yale&INFN Torino)
45
Fragmentation Functions
“trigger” jet
No apparent modification of FF of recoil jets
with pTrec>25 GeV would imply non-interacting
jets, but:
“recoil” jet
Jet broadeningEnergy shift harder FF
Need to better determine the jet energy
Elena Bruna (Yale&INFN Torino)
46
Jet Yields in ALICE
Elena Bruna (Yale&INFN Torino)
47
DCal for Di-Jet analysis @ ALICE
Elena Bruna (Yale&INFN Torino)
48