Overview of jet physics results from ALICE Filip Krizek on behalf of the ALICE collaboration Nuclear Physics Institute of CAS krizek@ujf.cas.cz February 2019
Jets in heavy-ion collisions ◮ Hard scattered partons produce collimated sprays of particles ◮ Jet is a phenomenological object defined by an algorithm ◮ Well understood theoretically in pQCD in elementary reactions ◮ Jet quenching in presence of Quark-Gluon plasma p+p CMS, Phys. Rev. Lett. 107 (2011) 132001 2 F. Krizek
Jets in ALICE tracks | η | < 0 . 9, 0 ◦ < ϕ < 360 ◦ , p const ◮ Charged jets: > 150 MeV/ c T ◮ Jet reconstruction: .. anti- k T algorithm (FastJet package [1]) For given jet R , charged jet acceptance is ..... | η jet | < 0 . 9 − R [1] Cacciari et al., Eur. Phys. J. C 72 (2012) 1896. 3 F. Krizek
Quantification of medium-induced jet modification ◮ Inclusive observables ( p T spectra, high- p T hadron-jet correlations) ◮ Quantification of jet shapes by functions which depend on 4-momenta of constituents (angularity, p T D , jet mass,. . . ) � p T, i � κ � ∆ R jet, i � β � λ κ β = ................. [1] p T,jet R i ∈ constituents ◮ Clustering history (grooming, N-subjettiness) [1] A. J. Larkoski, J. Thaler, and W. J. Waalewijn, JHEP 11 (2014) 129 4 F. Krizek
Selection of jets using fragmentation bias 1 ALICE Pb-Pb s =2.76 TeV -1 NN Inclusive ) c -1 10 Leading track p > 5 GeV/ c (GeV/ T Leading track p > 10 GeV/ c -2 T 10 Centrality: 0-10% -3 10 jet Charged Jets η -4 ch jet 10 Anti- k R = 0.3 d T | | < 0.5 η T,ch jet jet -5 10 N p track > 0.15 GeV/ c T 2 -6 d 10 p d -7 10 evt 1 -8 N 10 coll -9 1 10 N -10 10 -40 -20 0 20 40 60 80 100 raw p = p - A (GeV/ c ) ρ jet T,ch jet T,ch jet ch ALI−PUB−64210 ◮ Hard scattering, rare process embedded in large background ◮ Correction of jet transverse momentum for mean background energy density [1] p reco,ch = p ch,raw − ρ × A jet where A jet is jet area and T,jet T,jet ρ = median k T jets { p T,jet / A jet } ◮ Spectrum of reconstructed jets at low p T is dominated by combinatorial jets ◮ Suppression of combinatorial jets by high- p T jet constituent requirement results in fragmentation bias on jets [1] Cacciari et al., Phys. Lett. B 659 (2008) 119. 5 F. Krizek
Hadron-jet coincidence measurement 10 -1 TT = trigger track ) c ALICE 0-10% Pb-Pb s = 2.76 TeV (GeV/ NN 1 Anti- k charged jets, R = 0.4 T π − ∆ ϕ < 0.6 − 1 10 reco,ch TT{8,9} T,jet Integral: 1.644 0.005 ± − 2 N 10 p TT{20,50} TT { X,Y } means 2 d d Integral: 1.651 ± 0.009 jet η − 3 10 d X < p T,trig < Y GeV/ c trig 1 − 4 10 N − 5 10 Statistical errors only p reco,ch = p ch, raw − ρ × A jet T,jet T,jet − 6 10 40 20 0 20 40 60 80 100 120 − − reco,ch [1] ALICE, JHEP 09 (2015) 170 p (GeV/ c ) T,jet ALI−PUB−93509 ◮ Hadron-jet correlation allows to suppress combinatorial jets including multi-parton interaction without imposing fragmentation bias ◮ Data driven approach allows to measure jets with large R and low p T ◮ In events with a high- p T trigger hadron, analyze recoiling away side jets [1] | ϕ trig − ϕ jet − π | < 0 . 6 rad ◮ Assuming uncorrelated jets are independent of trigger p T 6 F. Krizek
∆ recoil in Pb–Pb at √ s NN = 2 . 76 TeV � � d 2 N jet d 2 N jet 1 − 1 � � ∆ recoil = � � d p ch d p ch N trig T,jet d η � N trig T,jet d η � p T,trig ∈ TT { 20 , 50 } p T,trig ∈ TT { 8 , 9 } d 2 N AA � � � σ AA → h+X · d 2 σ AA → h+jet+X 1 � 1 jet � � ⋄ Link to theory ... = � � N AA d p ch d p ch T,jet d η jet T,jet d η jet � � trig p T,trig ∈ TT p T,h ∈ TT − 2 -1 10 ◮ ∆ recoil corrected for background ) ALICE 0-10% Pb-Pb s = 2.76 TeV c (GeV/ Anti- k charged jets smearing of jet p T + detector T π − ∆ ϕ < 0.6 effects TT{20,50} − TT{8,9} recoil − 3 10 ◮ Medium effects ∆ ∆ I AA = ∆ Pb-Pb recoil / ∆ pp recoil R = 0.2 R = 0.4 Need pp reference at the same √ s − 4 R = 0.5 10 Correlated uncertainty Shape uncertainty 20 30 40 50 60 70 80 90 100 ALICE, JHEP 09 (2015) 170 ch (GeV/ ) p c T,jet ALI−PUB−93501 7 F. Krizek
∆ I AA and ∆ recoil ratio in Pb–Pb =0.5) 1.8 PYTHIA ALICE 1.6 ALICE R = 0 . 5 0-10%, Pb-Pb s = 2.76 TeV recoil 1.6 NN 0-10% Pb-Pb s = 2.76 TeV NN Anti- k charged jets 1.4 Anti- k charged jets, R = 0.5 T R T π − ∆ ϕ < 0.6 1.4 ∆ < 0.6 π − ∆ ϕ ( 1.2 recoil TT{20,50} − TT{8,9} / PbPb recoil TT{20,50} − TT{8,9} 1.2 1 ∆ 1 ∆ =0.2)/ Hadron Trigger Threshold = Hadron Trigger Threshold 0.8 AA 0.8 0.6 I 0.6 ∆ R 0.4 ( ALICE data 0.4 recoil ALICE data Shape uncertainty 0.2 Shape uncertainty Correlated uncertainty 0.2 PYTHIA Perugia: Tune 2010 & 2011 ∆ Correlated uncertainty 0 0 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100 ch p (GeV/ c ) ch p (GeV/ c ) T,jet T,jet ALI-PUB-93521 ALI−PUB−93497 ◮ Left: ∆ I AA with Reference ∆ PYTHIA from PYTHIA Perugia 10 recoil Suppression of the recoil jet yield ◮ Right: Observable sensitive to lateral energy distribution in jets Red band: variation in the observable calculated using PYTHIA tunes No evidence for significant energy redistribution w.r.t. PYTHIA ALICE, JHEP 09 (2015), 170 8 F. Krizek
Jet broadening and the transport coefficient ˆ q � � k 2 � d 2 k ⊥ = 1 ⊥ (2 π ) 2 k 2 ˆ q ≡ ⊥ P ( k ⊥ ) L L � q d 2 x ⊥ e − i k ⊥ x ⊥ W R ( x ⊥ ) P ( k ⊥ ) = k L W R ( x ⊥ ) ≡ expectation value of the Wilson loop ◮ Strongly coupled plasma (AdS CFT) : P ( k ⊥ ) is Gaussian ◮ Weakly coupled plasma (perturbative thermal field theory) : P ( k ⊥ ) is a Gaussian with a power-law P ( k ⊥ ) ∝ 1 / k 4 ⊥ tail emerging from single hard Moli` ere scatterings off QGP quasi-particles ⇒ Use recoil jets to search for QGP quasi-particles [1] by looking at enhancement in large angle deflections w.r.t. reference pp ........... [1] D’Eramo et al., JHEP 05 (2013) 031. 9 F. Krizek
Search for large-angle single hard Moli` ere scatterings ) ϕ ALICE ∆ 0.06 0-10% Pb-Pb s = 2.76 TeV ( NN Φ Anti- k charged jets, R = 0.4 T reco,ch 40 < p < 60 GeV/ c T,jet 0.04 Hadron trigger ALICE, JHEP 09 (2015), 170 TT{20,50} − TT{8,9} 0.02 TT{20,50} TT{8,9} 0 1.6 1.8 2 2.2 2.4 2.6 2.8 3 ∆ ϕ ALI−PUB−93873 For recoil jets in 40 < p ch T,jet < 60 GeV/ c define � � d 2 N jet d 2 N jet 1 1 � � Φ (∆ ϕ ) = − � � d p ch d p ch N trig T,jet d∆ ϕ � N trig T,jet d∆ ϕ � TT { 20 , 50 } TT { 8 , 9 } Quantify the rate of large angle scatterings � π − ∆ ϕ thresh Σ (∆ ϕ thresh ) = Φ (∆ ϕ ) d∆ ϕ π/ 2 10 F. Krizek
Σ (∆ ϕ thresh ) in Pb–Pb and PYTHIA 0.03 ) PYTHIA thresh ALICE ALICE Statistical errors only 2 0-10% Pb-Pb s = 2.76 TeV 0-10% Pb-Pb s = 2.76 TeV NN NN ϕ ) Anti- k charged jets, R = 0.4 thresh Anti- k charged jets, R = 0.4 T T ∆ reco,ch reco,ch 0.02 40 < p < 60 GeV/ c 40 < p < 60 GeV/ c ( T,jet T,jet Σ ϕ TT{20,50} TT{8,9} TT{20,50} TT{8,9} − − ∆ 1 Pb-Pb ( Σ / Data PYTHIA + Pb-Pb 0.01 ) thresh 0 ϕ ∆ 0 ( Σ Slope = 0.527 0.641( stat ) 0.36( sys ) Statistical errors only − ± ± 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.1 0.2 0.3 0.4 0.5 0.6 0.7 ∆ ϕ ∆ ϕ thresh thresh ALI−PUB−93885 ALI−PUB−93889 ◮ Raw data are compared with PYTHIA smeared with detector response and embedded into real events ◮ Ratio < 1 corresponds to the suppression of recoil jet yield ◮ Shape of the ratio depends on underlying processes ◮ Fit of the ratio by a linear function gives a slope consistent with zero ⇒ No evidence for medium-induced Moli` ere scattering ◮ To be further studied in Run3 with more statistics and for lower jet p T s ALICE, JHEP 09 (2015), 170 11 F. Krizek
QGP signatures in small systems ◮ Indication of collective effects in pp and p–Pb 2 < p < 4 GeV/ c p-Pb s = 5.02 TeV T,trig NN 1 < p < 2 GeV/ c (0-20%) - (60-100%) T,assoc -1 ) (rad 0.85 assoc ϕ ∆ 0.80 d η N ∆ 2 d d 0.75 trig 1 N 2 4 1 3 2 0 (rad) ∆ 1 η ϕ ∆ -1 0 -1 -2 ALI−PUB−46246 CMS, JHEP 09 (2010) 091 ALICE, Phys.Lett. B 719 (2013) 29–41 ◮ Is there jet quenching in p–Pb? qL 2 ⋄ ∆ E ∝ ˆ BDMPS, Nucl. Phys. B483 (1997) 291 q | pPb = 1 ⋄ ˆ 7 ˆ q | PbPb K.Tywoniuk, Nucl.Phys. A 926 (2014) 85–91 ⋄ ∆ E = (8 ± 2 stat ) GeV / c medium-induced E transport to R > 0 . 5 in Pb–Pb ALICE, JHEP 09 (2015) 170 12 F. Krizek
Event Activity biased jet measurements in p–Pb at LHC Jet R pPb in p–Pb at √ s NN = 5 . 02 TeV Event Activity from E T in Pb-going direction − 4 . 9 < η < − 3 . 2 d N cent jets / d p T R pPb = T pPb · d σ pp / d p T ◮ R pPb depends on rapidity range Caveats: ◮ T pPb assume Event Activity correlated with geometry (Glauber modeling) ◮ Conservation laws and fluctuations Kordell, Majumder, arXiv:1601.02595v1 Alternative: Hadron-jet conditional yields 13 F. Krizek
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