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Charged Particle Jet measurements with the ALICE Experiment in pp collisions at the LHC Sidharth Kumar Prasad Wayne State University, USA for the ALICE.

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Presentation on theme: "Charged Particle Jet measurements with the ALICE Experiment in pp collisions at the LHC Sidharth Kumar Prasad Wayne State University, USA for the ALICE."— Presentation transcript:

1 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 15/5/2015WWND-2012 Puerto Rico (UAS)

2 25/5/2015WWND-2012 Puerto Rico (UAS)  Physics motivation  Jet shape observables  Data analysis  Results  Summary Outline

3 3 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 p t 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. 5/5/2015WWND-2012 Puerto Rico (UAS)

4 5/5/2015WWND-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, Sterman Nucl. Phys. B263 (1986) 37 Jets: Connection between theory and experiment

5 5/5/2015WWND-2012 Puerto Rico (UAS)5 Jet shape observables r r+Δr PTsum  Charged particle multiplicity in leading jet:  Average radius containing 80% of jet p t :  Radial distribution of transverse momentum about the jet axis:

6 65/5/2015WWND-2012 Puerto Rico (UAS) CDF Collaboration (p t ) CDF Collaboration (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 (p t ) Jet shape observables

7 5/5/2015WWND-2012 Puerto Rico (UAS)7 The ALICE detectors TPC ITS Charged particle tracking: Using Time Projection Chamber (TPC) + Inner Tracking System (ITS). |η| < < Φ < 2π

8 5/5/2015WWND-2012 Puerto Rico (UAS)8 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.  p t (track) > GeV/c.  | η (track) | < 0.9  Jet Reconstruction: Using anti-kt*, a sequential recombination algorithm from FASTJET.  R = 0.4  20 < p t (jet) < 100 GeV/c  | η (jet) | < 0.5 *[M. Cacciari and P. Salam, arXiv: v1[hep-ph], 2008]

9 9 Results

10 10 Comparing results from SISCone, anti-kt, and kt jet finders Good agreement between various jet finders within ±15%  Results from anti- k t, k t and SISCone agree with each other.  Therefore, analysis is performed using anti-k t only. 5/5/2015WWND-2012 Puerto Rico (UAS)

11 115/5/2015WWND-2012 Puerto Rico (UAS) Uncorrected charged jet p t distribution  Reasonable agreement between uncorrected data and PYTHIA- Perugi0 (detector level).

12 12 Average number of charged particles in leading jet 5/5/2015WWND-2012 Puerto Rico (UAS)  Charged track multiplicity inside jets increases with increasing jet p t.  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).

13 13  High p t 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). Radial distribution of jet momentum about jet axis

14 145/5/2015WWND-2012 Puerto Rico (UAS)  80% of the jet energy is contained within a cone of radius ~0.16 at low p t which decreases towards high p t showing that jets become narrower at high p t.  Good agreement between data and PYTHIA (within ±10%).  Gray bands show systematic uncertainty.  Bin-by-bin correction is done using PYTHIA. Average R containing 80% of jet p T w.r.t. cone of R=0.4

15 15  Results on charged particle jet measurements using ALICE, in pp collisions at 7 TeV, are presented.  The results obtained using SISCone, anti-k t, and k t jet finding algorithms are found to be in good agreement with each other within ±15%.  Mean multiplicity in leading charged jet ( ), increases with increasing jet p t, consistent with predictions from PYTHIA. 5/5/2015WWND-2012 Puerto Rico (UAS) Summary

16 16  High p t jets are more collimated as compared to low p t jets.  Results obtained on jet shape observables are in agreement with predictions from PYTHIA (Perugia0) within ±10% and qualitatively with earlier measurements. 5/5/2015WWND-2012 Puerto Rico (UAS) Summary

17 17 Backup slides 5/5/2015WWND-2012 Puerto Rico (UAS)

18 18  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)

19 19  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 i th and j th 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

20 20  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 E T ->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 E T ->0 parton may wind up in one of the high E T jet or remain by itself, but in the limit of its E T ->0 does not affect the transverse energy and direction of the high E T jet. Thus successive recombination algorithms are ‘infrared collinear safe’. Phys. Rev. D, Vol.48, Number 7, 1993

21 21 Average number of charged particles in leading jet (PYTHIA detector level Compared with PYTHIA particle level) (The ratio is used as correction factor) 5/5/2015WWND-2012 Puerto Rico (UAS)

22 22 Average R containing 80% of jet pT PYTHIA detector level Compared with PYTHIA particle level (The ratio is used as correction factor) 5/5/2015WWND-2012 Puerto Rico (UAS)

23 5/5/2015WWND-2012 Puerto Rico (UAS)23 Jet finding algorithms Jets are operationally defined on the basis of clustering algorithms. k ti,j = particle transverse momentum (p t ) k t : p>0 (soft particles merged first) anti-k t : p<0 (hard particles merged first) R = resolution parameter  Sequential Recombination Clusterizer:  FASTJET k t, *  FASTJET anti-k t **  SISCone: A seedless cone algorithm *** * [S. D. Ellis and D. E. Soper, Phys. Rev. D 48, 3160, 1993] ** [M. Cacciari and P. Salam, arXiv: v1[hep-ph], 2008] *** [G. P. Salam and G. Soyez, arXiv: v2[hep-ph], 2007]


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