Charged Particle Jet measurements with the
ALICE Experiment in pp collisions at the LHC
Sidharth Kumar Prasad
Wayne State University, USA
for the ALICE Collaboration
4/7/2015
WWND-2012 Puerto Rico (UAS)
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Outline
 Physics motivation
 Jet shape observables
 Data analysis
 Results
 Summary
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Physics Motivation
 Jets are the collimated spray of
particles originating from the
fragmentation of hard scattered
partons in pp collisions.
 Jets provide a proxy to high pt
hard partons in elementary hard
scattering.
 Jets are used to test the
perturbative quantum
chromodynamics (pQCD) and its
implementations in event
generators.
 Jets are used in A+A collisions to
probe the properties of the hot
and dense medium produced.
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Jets: Connection between theory and experiment
Jets are the experimental tools
for understanding the parton
kinematics.
pQCD calculates partons.
Experiments measure hadrons.
Re-associate measurable hadrons
to accurately reconstruct parton
kinematics.
Tools: jet finding algorithms. Apply
same algorithm to data and
theoretical calculations.
pQCD factorization/jet spectrum
PDF
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Partonic x-section
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Collins, Soper, Sterman
Nucl. Phys. B263 (1986) 37
Fragmentation function
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Jet shape observables
 Charged particle multiplicity in leading jet: < N ch > ( pt ) =
 Radial distribution of transverse
momentum about the jet axis:
å(å p )
t
< ptsum > (r) =
jets
åN
ch
jets
N jets
PTsum
r+Δr
r
N jets
 Average radius containing 80% of jet pt:
åR
80
< R80 > ( pt ) =
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jets
N jets
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Jet shape observables
<pTsum>(r)
 Jet shapes provides details of the
parton-to-jet fragmentation process*.
pp( s =1.8TeV)
* S. D. Ellis et.al.,Phys. Rev. Lett. 69, 3615, 1992
CDF Collaboration
CDF Collaboration, Phys. Rev. D 65 092002
<R>(pt)
pp( s =1.8TeV )
<Nch>(pt)
pp( s =1.8TeV )
pp( s = 1.8TeV )
CDF Collaboration
CDF
Collaboration
CDF Collaboration
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The ALICE detectors
TPC
ITS
Charged particle tracking: Using Time Projection
Chamber (TPC) + Inner Tracking System (ITS).
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|η| < 0.9
0 < Φ < 2π
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Data Analysis
 Dataset: 2010 LHC data production for pp collisions at 7 TeV.
 Event selection: Events with minimum bias trigger condition with
vertex z-position within ±10 cm from nominal interaction point.
 Track selection: Based on information from TPC and ITS.
 pt (track) > 0.150 GeV/c.
 | η (track) | < 0.9
 Jet Reconstruction: Using anti-kt*, a sequential recombination
algorithm from FASTJET.
 R = 0.4
 20 < pt (jet) < 100 GeV/c
 | η (jet) | < 0.5
*[M. Cacciari and P. Salam, arXiv:0802.1189v1[hep-ph], 2008]
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Results
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Comparing results from SISCone, anti-kt, and kt jet finders
Good agreement
between various jet
finders within ±15%
 Results from antikt, kt and SISCone
agree with each
other.
 Therefore,
analysis is
performed using
anti-kt only.
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Uncorrected charged jet pt distribution
 Reasonable
agreement between
uncorrected data
and PYTHIAPerugi0 (detector
level).
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Average number of charged particles in leading jet
 Charged track multiplicity
inside jets increases with
increasing jet pt.
 Bin-by-bin correction is
done using PYTHIA
(Perugia0).
Good agreement between
data and PYTHIA (within
±10%).
 Gray bands show
systematic uncertainty (mostly
coming from uncertainty in
tracking efficiency).
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Radial distribution of jet momentum about jet axis
 High pt jets are more collimated than low pt jets.
 Good agreement between data and PYTHIA (within ±10%).
Bin-by-bin correction is done using PYTHIA (Perugia0).
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Average R containing 80% of jet pT w.r.t. cone of R=0.4
 80% of the jet energy is
contained within a cone of
radius ~0.16 at low pt which
decreases towards high pt
showing that jets become
narrower at high pt.
 Good agreement between
data and PYTHIA (within
±10%).
 Gray bands show
systematic uncertainty.
 Bin-by-bin correction is
done using PYTHIA.
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Summary
 Results on charged particle jet measurements using
ALICE, in pp collisions at 7 TeV, are presented.
 The results obtained using SISCone, anti-kt, and kt jet
finding algorithms are found to be in good agreement
with each other within ±15%.
 Mean multiplicity in leading charged jet (<Nch>),
increases with increasing jet pt, consistent with
predictions from PYTHIA.
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Summary
 High pt jets are more collimated as compared to low pt
jets.
 Results obtained on jet shape observables are in
agreement with predictions from PYTHIA (Perugia0)
within ±10% and qualitatively with earlier
measurements.
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Backup slides
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Cone Algorithm (UA1)
 A list of seed objects is made which are above certain threshold
 At hardest seed location cone of radius R is constructed and
momenta of all particles within the cone are summed up
 The direction of the resulting sum is then used as a new seed
direction and the process is iterated until the direction is stable.
 Declare this as a jet and remove all the particles from the list
 Start with the next hardest particle in the list, find the next jet and
continue till no particles left in the list (IC-PR : Iterative cone with
progressive removal)
 Fixed Cone Progressive Removal (FC-PR): Do not iterate the
cone direction
 Identify a fixed cone around the seed starting from the hardest
one and call it a jet, remove all the particles from the list, find the
fixed cone around the next hardest particle in the list till no particle
left in the list.
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Successive recombination algorithms
 Each object to be clustered is considered as a proto-jet and a
list of proto-jets is made
 For each proto-jet a quantity (a) kT2,i = pT2 ,ip is calculated.
 For each pair of proto-jets following quantity is calculated,
(b)
min(kT2,ip , kT2,pj )DR 2
dij =
D2
where ΔR is the distance between ith and jth protojet and D is the
cone size parameter. p=1 (kt), p=-1(anti-kt)).
 A comparison between (a) and (b) is made.
 If (a) is the smallest of these quantities then, that protojet
becomes a jet and is removed from the list
 If (b) is the smallest quantity then the two projets ‘i’ and ‘j’ are
merged into a single protojet and the original two protojets are
removed from the list
 The process is iterated until all protojets become jets.
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Infrared and collinear safe/unsafe
 The infrared divergences in Feynman diagrams come from the
configurations in which
 a parton emits a soft gluon
 an incoming parton emits another parton that carries a
fraction of longitudinal momentum but no transverse momentum
 The probability for one of these configurations to occur is infrared
sensitive and infinite in fixed order perturbation theory
 However, unitarity dictates that the sum of the probabilities for one
of these configurations to happen or not to happen is 1.
 Therefore, infrared safety is achieved if the measured jet variables
do not change when an ET->0 parton is emitted or when a parton
divides into collinear partons
In successive recombination algorithms, if a parton divides into two
partons of collinear momenta, then the algorithm combines them.
Similarly ET->0 parton may wind up in one of the high ET jet or remain by
itself, but in the limit of its ET->0 does not affect the transverse energy
and direction of the high ET jet. Thus successive recombination
algorithms are ‘infrared collinear safe’.
Phys. Rev. D, Vol.48, Number 7, 1993
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Average number of charged particles in leading jet
(PYTHIA detector level Compared with PYTHIA particle level)
(The ratio is used as correction factor)
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Average R containing 80% of jet pT
PYTHIA detector level Compared with PYTHIA particle level
(The ratio is used as correction factor)
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Jet finding algorithms
Jets are operationally defined on
the basis of clustering algorithms.
 Sequential Recombination Clusterizer:
 FASTJET kt,*
 FASTJET anti-kt**
dij = min(kti2 p, ktj2 p )(Dyij2 + Dfij2 ) / R2
kti,j= particle transverse momentum (pt)
kt: p>0 (soft particles merged first)
anti-kt: p<0 (hard particles merged first)
R = resolution parameter
 SISCone: A seedless cone algorithm***
* [S. D. Ellis and D. E. Soper, Phys. Rev. D 48, 3160, 1993]
** [M. Cacciari and P. Salam, arXiv:0802.1189v1[hep-ph], 2008]
*** [G. P. Salam and G. Soyez, arXiv:0704.0292v2[hep-ph], 2007]
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