Martin Spousta for the ATLAS Collaboration

1. Jet results and jet reconstruction techniques in pp and their prospects in heavy-ion collisions in ATLAS Martin Spousta for the ATLAS Collaboration

2. Outline • Motivation • Choice of the jet algorithm • Measurement of jet energy spectra in heavy ions • jet energy scale • jet energy resolution • Measurement of jet internal structure in heavy ions • jet shapes • fragmentation function and jT distribution • complementary quantities to study the energy loss • Recent results from jet measurement in pp collisions at 7 TeV • inclusive jet cross section • jet shapes • Summary Martin Spousta, Charles University in Prague

3. Motivation: Why ATLAS? jet reconstruction using calorimeter, full azimuth, 10 units of pseudorapidity ZDC to estimate centrality and reaction plane and to measure UPC first layer of LAr EM calorimeter excellent for photon isolation, other layers also well segmented tracking over 5 units of pseudorapidity – fragmentation studies Martin Spousta, Charles University in Prague

4. Motivation: Expected jet rates • Pb+Pb (<Ncoll> = 440) • √sNN=2.75 TeV • jets with R=0.4 • ∫L = 25 μb-1 (one year) • σPb+Pb/σp+p=100 • full acceptance |η|<4.5 • Assuming 2 weeks of HI running means 1/20 of nominal luminosity (∫L = 25 mb-1) • Full ATLAS acceptance, 100% trigger efficiency • Reasonable rates • 4000 jets with ET> 100 GeV • 50 jets with ET > 200 GeV by W. Vogelsang Martin Spousta, Charles University in Prague

5.    Jet reconstruction strategy Background / underlying event subtraction Seeded iterative cone algorithm First subtract the background,then reconstruct jets Cone-type Infrared & collinear safe SISCone algorithm Jet finding algorithm kT algorithm(k=1) Cluster-type Cambridge/Aachen(k=0) First reconstruct jets,then separate signal jets from background and subtract the background anti-kT algorithm(k=-1) Martin Spousta, Charles University in Prague

6. Comparison among jet finding algorithms • Which jet algorithm we should use? How the UE influences the jet finding? • kT algorithm exhibits serious problems with the jet energy scale: for more severe background, kT underestimates the jet area and thus the energy. This is due to the fact that kT preferably clusters the softer part of a jet with the background • Problematic algorithms: - kT algorithm, - Cambridge/Aachen • Non-problematic: - anti-kT algorithm, - cone algorithm jet energy scale kT algorithm size of the fluctuations in bkgr Toy model: Poisson-like background (“UE”) randomly generated to allow variation of mean and rms of ET 20% underestimation mean bkgr Et Martin Spousta, Charles University in Prague

7. Comparison among jet finding algorithms • Which jet algorithm we should use? How the UE influences the jet finding? • kT algorithm exhibits serious problems with the jet energy scale: for more severe background, kT underestimates the jet area and thus the energy. This is due to the fact that kT preferably clusters the softer part of a jet with the background • Problematic algorithms: - kT algorithm, - Cambridge/Aachen • Non-problematic: - anti-kT algorithm, - cone algorithm jet energy scale anti-kT algorithm size of the fluctuations in bkgr Toy model: Poisson-like background (“UE”) randomly generated to allow variation of mean and rms of ET no problems with jet energy scale mean bkgr Et Martin Spousta, Charles University in Prague

8. Comparison among jet finding algorithms Area of jets delivered by different algorithms is very different in the noisy environment f f Jet ID number Jet ID number jet ET (GeV) the same event h h • anti-kT algorithm, R=0.4 • conical jets with the average radius of R • kT algorithm, R=0.4 • smaller jets with irregular areas • denser UE leads to smaller jet areas Martin Spousta, Charles University in Prague

9. Characteristic properties of anti-kT algorithm Let’s look at multiplicity of particles as a function of the distance from the jet axis (charged PYTHIA particles, MinBias pp collisions, √s = 900 GeV) We see a“Mach-cone”! Mach cone? No. This is just an effect of optimization of jet axis with respect to the jet periphery ... characteristic for the anti-kT algorithm. One needs to be careful about systematic biases coming from usage of a given jet algorithm. Martin Spousta, Charles University in Prague

10. Jet energy resolution stochastic term noise term Fitting well known formula: to p+p and Pb+Pb energy resolution constant term ATLAS Preliminary ATLAS Preliminary |h|<5.0 p+p Pb+Pb b=2fm, dN/dh~2700 truth jet ET (GeV) truth jet ET (GeV) • Jet energy resolution below 25% for 70 GeV jets in the most central collisions (dN/dh~2700  b=2 fm, unquenched HIJING, cone algorithm) • Irreducible background fluctuations from HIJING simulations: ~ 15 GeV / jet ... ... but reality can be very different ... Martin Spousta, Charles University in Prague

11. |h|<5.0 Real heavy ion underlying event p+p Jet energy resolution ... fluctuations in the underlying event are highly model dependent! ATLAS Preliminary <ET>=1.8 GeV, s(ET)=0.8 GeV <ET>=1.3 GeV, s(ET)=0.6 GeV s(ET)=0.8 GeV means p1~15 GeV s(ET)=0.6 GeV means p1~11 GeV! Realistic jet energy resolution in PbPb — HIJING — HYDJET b = (0-3) fm, |h|<5  = , PYTHIA truth jet ET (GeV) calorimeter tower ET (MeV)  Once we know real fluctuations we can easily compute limiting jet energy resolution in PbPb collisions! Martin Spousta, Charles University in Prague

12. ATLAS Preliminary ATLAS Preliminary Simulation of central PbPb collisions Simulation of central PbPb collisions ○ - truth ● - reconstructed ▼ - background (jet ET 70-140 GeV) ○ - truth ● - reconstructed ▼ - background (jet ET 70-140 GeV) jT z Fragmentation function and jT distribution:unquenched jets • Reconstruction procedure: • tracks are matched to calorimeter towers of a jet • jT and z for tracks with pT>2 GeV is computed • background distributions of jT and z are computed using tracks that match with HIJING particles, these distributions are subtracted, correction for the jet position resolution is applied … we can well reproduce jT distribution and fragmentation function in the presence of heavy-ion UE Martin Spousta, Charles University in Prague

13. Reconstructed ○ - unquenched ● - quenched (jet ET=70-140 GeV) Reconstructed ○ - unquenched ● - quenched (jet ET=70-140 GeV) ATLAS Preliminary ATLAS Preliminary Fragmentation function and jT distribution:quenched jets Low z enhanced, higher zsuppressed  leading particle suppressed, redistribution of energy out of a jet core Large jT suppressed  gluons radiated from large angles Effect of jet quenching simulated by PYQUEN (without heavy ion UE) ...... if the quenching is of the order of PYQUEN prediction we should be able to measure it Martin Spousta, Charles University in Prague

14. Simulation of pp jets (jet ET 70-140 GeV) Simulation of pp jets (jet ET 70-140 GeV) ATLAS Preliminary ATLAS Preliminary Fragmentation function and jT distribution: detector / UE effects • black = reference, computed using simulation of pp jets, <ET>=60 GeV • open = smearing of the jet axis by convoluting with gaussian s(DR)=0.06 - leads to: characteristic shift in jT distribution; fragmentation function remains almost unaffected • blue = simulation of jet energy scale shift by +10% - leads to:characteristic shift in fragmentation function; jT distribution remains unaffectedUE or detector effects could mimic the jet quenching – careful cross-checks are needed. Martin Spousta, Charles University in Prague

15. jet axis Jet shapes • Measure the energy flow inside the jet at the calorimeter level • First jet shapes already measured in 7 TeV pp collisions at ATLAS! More details later ... integral jet shape ... differential jet shape Martin Spousta, Charles University in Prague

16. Jet shape measurements: UE / detector effects jet shape jet energy flow Simulation of pp jets (jet ET 70-140 GeV) Simulation of pp jets (jet ET 70-140 GeV) ATLAS Preliminary ATLAS Preliminary • black = reference jet shape computed using simulated pp jets • red = adding gaussian noise with <ET>=0, s(ET)=1.4 GeV leads to: • jet energy resolution deterioration, s(DEt/Et) = 20% • jet position resolution deterioration s(DR) = 0.06 • characteristic change in the jet shape, and pT spectra • green = we can recover the jet shape e.g. by re-computing the jet axis using simple cone algorithm with R randomly chosen in <0.1,0.5> Martin Spousta, Charles University in Prague

17. Jet shape measurements: UE / detector effects Simulation of pp jets jet ET 70-140 GeV Simulation of pp jets jet ET 70-140 GeV ○ - particle level ● - detector level ○ - particle level● - detector level□ - bayesian □ - bin-by-bin ATLAS Preliminary ATLAS Preliminary Effect of the calorimeter can be corrected by unfolding (bin-by-bin as good as bayesian unfolding) Martin Spousta, Charles University in Prague

18. Modification of jet shapes ATLAS Preliminary ATLAS Preliminary PbPb – no quenching(jet ET = 70-140 GeV) (jet ET = 70-140 GeV) • Jet shape can be well measured – it is almost centrality independent (after applying corrections on jet position resolution) • Jet quenching effect predicted by PYQUEN is well measurable Martin Spousta, Charles University in Prague

19. jet shape jet energy spectrum ○ - non-quenched ● - quenched PYQUEN with only radiative energy loss  - non-quenched  - quenched jet energy spectrum jet shape PYQUEN with only collisional energy loss ○ - non-quenched ● - quenched  - non-quenched  - quenched Complementary quantities to measure different energy loss mechanisms ATLAS Preliminary ATLAS Preliminary ATLAS Preliminary ATLAS Preliminary  We need to use different quantities to learn about energy loss mechanism Martin Spousta, Charles University in Prague

20. perturbative effects estimated and summed in quadrature (orange band). • Main contribution to the systematic errors (grey band) is due to the jet energy scale uncertainty which is below 9% in the entire pT and rapidity range (below 7% for the central jets with |h|<0.8). Inclusive jet cross section measured in pp collisions at 7 TeV • Inclusive jet differential cross section measured in pp collisions at 7 TeV using L ~ 17 nb-1 (uncertainty of 11%). • Jets defined using R=0.6 anti-kT algorithm. Topological calorimeter clusters used in the input for the jet reconstruction. • Data corrected for detector effects using bin-by-bin correction and compared to NLOJET++ with CTEQ6.6 PDFs. • Theory uncertainties due to the choice of PDFs, renormalization and factorization scheme, as, and non-perturbative effects estimated and summed in quadrature (orange band) • jet energy scale < 9% Martin Spousta, Charles University in Prague

21. Inclusive jet cross section measured in pp collisions at 7 TeV • Double differential single inclusive jet cross section for different rapidity bins as a function of pT (left) and for different pT bins as a function of rapidity (right). • Data matches theory within systematic and statistical errors in all pT and rapidity bins. Martin Spousta, Charles University in Prague

22. Jet shapes in pp collisions at 7 TeV • Jet shapes measured at the calorimeter level (left) and using charged tracks (right). • Differential jet shape measured using anti-kT jets with R=0.6. Data compared with detector level MC simulation. • Data agrees with Monte Carlo up to 10% in the whole pT,jet range of 40 – 210 GeV and rapidity range of |y| < 2.8. • ATLAS tune of Herwig + Jimmy provide the best description of the jet shapes. Martin Spousta, Charles University in Prague

23. Summary • ATLAS detector is well suited for jet studies in heavy-ion collisions (it is also well suited for soft heavy ion physics, quarkonia, ultra-peripheral collisions, and other important measurements). • We have stressed some caveats of the measurement of jets and their properties that can influence our understanding of the jet energy loss mechanism. • We have shown first results on inclusive single jet cross section measurement and jet shape measurement in pp collisions at √sNN = 7 TeV. Martin Spousta, Charles University in Prague

24. Backup Backup Martin Spousta, Charles University in Prague

25. Jet reconstruction strategy PYTHIA dijets embedded into the unquenched HIJING events • Cone jet reconstruction: • regions of interest found (seed regions) – fast sliding window algorithm used (size of the sliding window 0.3x0.3) • background computed • excluding the seed-regions (R=0.8 outside of a seed) • vs. h (binning of 0.1) • vs. layer (24 calorimeter layers) • background subtracted • standard p+p jet finding algorithm used (seeded iterative R=0.4 cone algorithm) one event before the background subtraction one event after the background subtraction An alternative: (anti)kT-algorithm based reconstruction strategy Martin Spousta, Charles University in Prague

26. Fast (anti)kT jet reconstruction strategy • (anti)kT jet reconstruction: • run fast (anti)kT algorithm • separate “signal” jets from the “background” jets • use regions outside of signal jets to estimate the background • subtract the background from jets • ... motivated by Cacciari and Salam • An alternative: Use the same procedure as for the cone algorithm (previous slide) but subtract the background after the jet finding. Martin Spousta, Charles University in Prague

27. Jet energy scale and efficiency PbPb b=2 fm, dN/dh~2700 dN/dh~2700 ATLAS Preliminary ATLAS Preliminary • Jet energy scale within 5% above 60 GeV in the most central collisions simulated by “unquenched” HIJING (dN/dh~2700  b=2 fm). Cone algorithm R=0.4. • Below 60 GeV the efficiency is steeply decreasing  jet energy scale shift (it is more probable to find jets with higher energy) •  To trust any jet observation we need to have efficiency under the control. Martin Spousta, Charles University in Prague

28. Jet position resolution Pb+Pb dN/dh~2700 Pb+Pb dN/dh~2700 Tower size Cone algorithm Cone algorithm ATLAS Preliminary ATLAS Preliminary • Jet position resolution in f similar to that in h (full field simulated) • It improves with increasing jet energy, in the whole energy range jet position resolution is better than half of the tower size • Jet position resolution can be improved using smaller cones: jet axis of a reconstructed jet is substituted by the jet axis from jet reconstructed with R<Rorig Martin Spousta, Charles University in Prague

29. Efficiency and fake-rate ATLAS Preliminary Cone algorithm Cone algorithm ATLAS Preliminary • Efficiency is almost centrality independent – easier interpretation of jet properties vs. centrality • Above 70 GeV the efficiency is above 90% • Above 70 GeV very low fake rate < 5% (without any fake rejection) Martin Spousta, Charles University in Prague

30. Dijet correlations ATLAS Preliminary ATLAS Preliminary Cone algorithm Cone algorithm • associated and leading jets with ET>100 GeV Martin Spousta, Charles University in Prague

31. Tracking – resolution, efficiency ATLAS Preliminary ATLAS Preliminary ATLAS Preliminary 70% ATLAS Preliminary Martin Spousta, Charles University in Prague

32. Fake jet reduction ATLAS Preliminary ATLAS Preliminary Backup slides Martin Spousta, Charles University in Prague

33. Statistics only the most central collisions x jet quenching effect simulated with 5000 PYQUEN events Martin Spousta, Charles University in Prague

34. pp vs PbPb – important parameters Martin Spousta, Charles University in Prague