Jet splitting measurements in Pb--Pb and pp collisions at s √ NN = 5.02 TeV with ALICE
NNuclear Physics A 00 (2020) 1–4
NuclearPhysics A / locate / procedia XXVIIIth International Conference on Ultrarelativistic Nucleus-Nucleus Collisions(Quark Matter 2019)
Jet splitting measurements in Pb–Pb and pp collisions at √ s NN = Laura Havener on behalf of the ALICE collaboration
Yale University
Abstract
Recent ALICE measurements of jet splittings in Pb–Pb and pp collisions at √ s NN = ff erent scales. These include e ff ects such as multiple soft-radiation, single hard emissions, and color coherence.The Lund plane diagram is shown, including projections onto distributions of the splitting scale k T in intervals of thesplitting angle R g . Soft Drop grooming is applied to access hard splittings within the jet, enabling measurements ofgroomed substructure variables. These include the shared momentum fraction z g between the two hardest subjets andthe number of Soft Drop splittings n SD . The results in Pb–Pb collisions are compared to PYTHIA events embeddedinto a Pb–Pb background to separate out background from in-medium e ff ects. Measurements of z g and the normalizedsplitting angle θ g will also be shown in pp collisions at √ s = ff erent grooming settings. Keywords:
Nuclear Physics, Heavy-Ion Collisions, Quark-Gluon Plasma, QCD, Jets, Jet Quenching, Jet Substructure
1. Introduction
Jet substructure measurements investigate how the internal structure of a jet is modified by the mediumproduced in heavy-ion collisions. Specifically, they probe the phase space of jet emissions in search ofmedium-induced signals from e ff ects such as color coherence, multiple soft radiation, and single hard emis-sions using the Lund plane [1, 2]. The Lund plane is a 2D diagram in ln k T and ln 1 / ∆ R , where k T is therelative transverse momentum and ∆ R = (cid:112) ( η − η ) + ( φ − φ ) is the angular distance between two sub-jets 1 and 2. The Lund diagram is constructed experimentally by finding jets using the anti- k t algorithm [3],re-clustering them with the Cambridge / Aachen (C / A) algorithm [4] to enforce angular ordering, and fillingthe diagram with information about the first hard splittings. It can be used to separate out di ff erent types ofjets, including jets less impacted by the background, through grooming procedures.Soft Drop (SD) grooming [5] is used to access hard splittings within a jet to measure groomed sub-structure variables. This procedure finds the first splitting in a declustered C / A jet that passes a groomingcondition on the shared transverse momentum ( p T ) fraction between the subjets z = min( p T1 , p T2 ) p T1 + p T2 , where p T1 and p T2 are the higher and lower p T subjets, respectively. The SD grooming condition is defined as a r X i v : . [ nu c l - e x ] A p r / Nuclear Physics A 00 (2020) 1–4 z > z cut (cid:16) R g R (cid:17) β , where R g is the ∆ R for the subjets passing SD, R is the jet radius, and β and z cut are tuneableparameters with default values of 0 and 0.1, respectively. The z and k T of the groomed jets are denoted z g and k Tg , where smaller and larger z g values correspond to more asymmetric and symmetric splittings, respec-tively. The R g (or θ g = R g / R ) separates wider and narrower angle splittings which have di ff erent formationtimes with wide splittings forming earlier and narrow splittings forming later. The earlier emissions couldsee more of the medium and experience more modification. This measurement also uses iterative SD whichfollows the hardest branch to determine which splittings pass SD instead of stopping at the first, where n SD is the number of splittings that pass SD as the splittings are unwound. ALICE previously measured jetsubstructure in Pb–Pb collisions at √ s NN = √ s NN = ff erential studies.
2. Analysis
This analysis uses 2018 0–10% Pb–Pb and 2017 pp collision data from the ALICE detector [7] at √ s NN = .
02 TeV. The Pb–Pb data is not unfolded for detector e ff ects, but is compared to PYTHIA8 [8](Monash Tune) Monte Carlo (MC) simulations embedded into real Pb–Pb data such that the MC has thesame background as the data. The pp data is unfolded for detector e ff ects using 2D Bayesian unfold-ing [9] with a response built from PYTHIA8 generated jets that were propagated through a GEANT3 sim-ulation [10] of the ALICE detector. Track-based jets are reconstructed from tracks with p T >
150 MeV / c using the anti- k T algorithm with R = .
4. Jets in Pb–Pb collisions have a large background contribution thatis removed through the jet-by-jet constituent subtraction method [11].
3. Results
The Lund Plane is measured in 0–10% Pb–Pb collisions and is compared to embedded MC with SDgrooming applied. The distributions are subtracted from each other to remove additional background e ff ectsand is shown on the left in Fig. 1 for jets in the p T range 80–120 GeV / c . Both distributions are normalized tothe total number of jets in the p T range 80–120 GeV / c before subtraction, such that anything above or belowzero represents an enhancement or a suppression. A suppression is seen at large ∆ R (or small ln 1 / ∆ R ) witha corresponding enhancement at small ∆ R . The right panel in Fig. 1 shows the projections onto the ln k T axis in intervals of ln 1 / ∆ R (motivated in Ref [1]). The smaller ∆ R intervals show an enhancement in thenon-perturbative regime (ln k T <
0) and the larger ∆ R intervals show a suppression. Fig. 1. Left: The di ff erence of the Lund plane (ln k T vs. ln 1 / ∆ R ) distributions for 0–10% Pb–Pb data and embedded MC for track-based jets in the p T range 80–120 GeV / c . The distributions are normalized to the number of jets in the p T range 80–120 GeV / c beforethe subtraction is performed such that the z-axis is the di ff erence in the per jet yield. Right: Projections onto the ln k T axis in intervalsof ln 1 / ∆ R . The fully corrected θ g and z g distributions in pp collisions are shown in Fig. 2 for jets in the p T range 40–60 GeV / c . This is shown for di ff erent grooming settings by varying β which is useful for constraining pQCD Nuclear Physics A 00 (2020) 1–4 calculations and non-perturbative e ff ects [12]. Increasing β increases the contribution of jets with widerangle, more asymmetric splittings. All the distributions are shown to be mostly consistent with PYTHIA8Monash MC simulations through the ratio in the bottom panel. These fully corrected pp measurements willserve as a baseline for future unfolded Pb–Pb measurements. Fig. 2. The fully corrected θ g (left) and z g (right) distribution for track-based jets in the p T range 40–60 GeV / c in pp collisions at √ s = ff erent grooming conditions. Both the data (markers) and PYTHIA8 simulations (lines) are shown with the ratio ofdata to MC in the bottom panel. The distributions are normalized to the number of jets in the p T range 40–60 GeV / c . Unfolding substructure variables is challenging in Pb–Pb collisions due to the large uncorrelated back-ground that leads to a large contribution from incorrect splittings. Therefore, embedded MC is used as areference such that background e ff ects are e ff ectively canceled in any comparisons. The left-most panel ofFig. 3 shows the z g distribution in 0–10% Pb–Pb collisions compared to embedded MC, with the ratio in thebottom panel. The ratio shows a slight suppression of more symmetric splittings. A selection on the k Tg ofthe splittings greater than 1 GeV / c (or ln k Tg >
0) is applied to remove non-perturbative splittings [13]. Thisis shown in the center left panel of Fig. 3, where a more significant suppression is observed. The two rightpanels show the z g distributions for di ff erent cuts on R g , where wider splittings are shown on the center right( R g > .
2) and narrower splittings on the far right ( R g < . Fig. 3. The z g distribution for track-based jets in the p T range 80–120 GeV / c in 0–10% Pb–Pb data in black compared to embeddedMC in red. The distributions are normalized to the total number of jets in the p T range 80–120 GeV / c , not just the ones that pass SD.The far left panel is for all splittings, the center left panel is for splittings with ln k T >
0, the center right is for wider splittings with R g > .
2, and the far right is for narrower splittings with R g < .
1. The bottom panels are the ratios of the data to the embedded MC. / Nuclear Physics A 00 (2020) 1–4
The n SD distributions for jets in the p T range 80–120 GeV / c in Pb–Pb collisions compared to embeddedMC are shown in Fig. 4 for all splittings on the left and splittings with ln k T > n SD values and a suppression at higher n SD values. Fig. 4. The n SD distribution for Pb–Pb data in black and enbedded MC in red for 0–10% centrality and track-based jets in the p T range80–120 GeV / c . The distributions are normalized to the number of jets in the p T range 80–120 GeV / c . The left panel is for all jets andthe right panel is for jets with ln k T >
0. The bottom panel is the ratio of the data to the MC.
4. Conclusions and Outlook
New measurements of fully corrected z g and θ g distributions are shown in pp collisions at √ s = z g in both the perturbative and non-perturbativeregime in intervals of the splitting angle for 0–10% Pb–Pb collisions at √ s NN = ff ected by the medium than narrower splittings and thus experience more modification. The n SD distributions show a significant modification in Pb–Pb collisions with an enhancement of smaller values anda suppression of larger values. The next step is to investigate methods to suppress the background splittingsin Pb–Pb collisions such that the substructure variables can be unfolded for detector e ff ects and compareddirectly to theoretical predictions in order to further constrain jet quenching models. References [1] F. A. Dreyer, G. P. Salam, G. Soyez, The Lund Jet Plane, JHEP 12 (2018) 064.[2] H. A. Andrews, et al., Novel tools and observables for jet physics in heavy-ion collisions. arXiv:1808.03689 .[3] M. Cacciari, G. P. Salam, G. Soyez, The anti- k t jet clustering algorithm, JHEP 04 (2008) 063.[4] Y. L. Dokshitzer, G. D. Leder, S. Moretti, B. R. Webber, Better jet clustering algorithms, JHEP 08 (1997) 001.[5] A. J. Larkoski, S. Marzani, G. Soyez, J. Thaler, Soft Drop, JHEP 05 (2014) 146.[6] S. Acharya, et al., Exploration of jet substructure using iterative declustering in pp and Pb-Pb collisions at LHC energies arXiv:1905.02512 .[7] K. Aamodt, et al., The ALICE experiment at the CERN LHC, JINST 3 (2008) S08002.[8] T. Sjostrand, S. Mrenna, P. Z. Skands, A Brief Introduction to PYTHIA 8.1, Comput. Phys. Commun. 178 (2008) 852–867.[9] RooUnfold http: // hepunx.rl.ac.uk / adye / software / unfold / RooUnfold.html.[10] R. Brun, F. Bruyant, M. Maire, A. C. McPherson, P. Zanarini, GEANT 3: user’s guide, CERN, Geneva, 1987.[11] P. Berta, M. Spousta, D. W. Miller, R. Leitner, Particle-level pileup subtraction for jets and jet shapes, JHEP 06 (2014) 092.[12] Z.-B. Kang, K. Lee, X. Liu, D. Neill, F. Ringer, The soft drop groomed jet radius at NLL. arXiv:1908.01783 .[13] L. Cunqueiro, M. PÅoskoÅ, Searching for the dead cone e ffff