charged particle jet measurements with the alice experiment in pp collisions at the lhc

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

104/20/23 WWND-2012 Puerto Rico (UAS)


Physics motivation

Jet shape observables

Data analysis

Results

Summary

Outline

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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.


04/20/23 WWND-2012 Puerto Rico (UAS) 4

Jets are the experimental tools for understanding the parton kinematics.

pQCD factorization/jet spectrum

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.

Collins, Soper, StermanNucl. Phys. B263 (1986) 37

Jets: Connection between theory and experiment



rr+Δr

PTsum

Charged particle multiplicity in leading jet:

Average radius containing 80% of jet pt:

Radial distribution of transverse momentum about the jet axis:


CDF CollaborationCDF Collaboration

CDF Collaboration

<Nch>(pt)

CDF Collaboration

<pTsum>(r)

Jet shapes provides details of the parton-to-jet fragmentation process*.

* S. D. Ellis et.al.,Phys. Rev. Lett. 69, 3615, 1992

CDF Collaboration, Phys. Rev. D 65 092002 <R>(pt)



The ALICE detectors

TPC

ITS

Charged particle tracking: Using Time Projection Chamber (TPC) + Inner Tracking System (ITS).

|η| < 0.9 0 < Φ < 2π


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 anti-kt, kt and SISCone agree with each other.

Therefore, analysis is performed using anti-kt only.



Uncorrected charged jet pt distribution

Reasonable agreement between uncorrected data and PYTHIA-Perugi0 (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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High pt jets are more collimated than low pt jets.


Bin-by-bin correction is done using PYTHIA (Perugia0).

Radial distribution of jet momentum about jet axis


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.


Gray bands show systematic uncertainty.

Bin-by-bin correction is done using PYTHIA.

Average R containing 80% of jet pT w.r.t. cone of R=0.4

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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.


Summary

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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.


Summary

17

Backup slides


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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.

Cone Algorithm (UA1)

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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) is calculated. For each pair of proto-jets following quantity is calculated,

(b)

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.

Successive recombination algorithms

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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

Infrared and collinear safe/unsafe

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 pTPYTHIA detector level Compared with PYTHIA particle level

(The ratio is used as correction factor)



Jet finding algorithmsJets are operationally defined on the basis of clustering algorithms.

kti,j= particle transverse momentum (pt)kt: p>0 (soft particles merged first)anti-kt: p<0 (hard particles merged first)R = resolution parameter

Sequential Recombination Clusterizer: FASTJET kt,*

FASTJET anti-kt**

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]

charged particle jet measurements with the alice experiment in pp collisions at the lhc

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