ATLAS Jet Physics Results and Jet Substructure in 2010 Data from the LHC

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Miller On behalf of The ATLAS Experiment SLAC National Accelerator Laboratory and Stanford University Northwest

1 ATLAS Jet Physics Results and Jet Substructure in 20 Data from the LHC With a particular focus on experimental issues and effects David W. Miller On behalf of The ATLAS Experiment SLAC National Accelerator Laboratory and Stanford University Northwest Terascale Research Projects Using jet substructure to find new physics at the LHC February 2, 2011 David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

2 Roadmap 1 Introduction Historical context Pile-up and underlying event at the LHC Experimental conditions at the energy frontier 2 Jets in ATLAS Jet finding, reconstruction in ATLAS JES uncertainty JES pile-up uncertainty and corrections Jet-vertex association for pile-up filtering 3 Jet physics at ATLAS in 20 Inclusive jet and di-jet cross-section Search for new particles in two-jet final states Jet shapes and corrections for pile-up Jet substructure observables in data New directions 4 Summary and conclusions David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

3 Introduction Roadmap 1 Introduction Historical context Pile-up and underlying event at the LHC Experimental conditions at the energy frontier 2 Jets in ATLAS Jet finding, reconstruction in ATLAS JES uncertainty JES pile-up uncertainty and corrections Jet-vertex association for pile-up filtering 3 Jet physics at ATLAS in 20 Inclusive jet and di-jet cross-section Search for new particles in two-jet final states Jet shapes and corrections for pile-up Jet substructure observables in data New directions 4 Summary and conclusions David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

4 Introduction Historical context First convincing evidence for jet production: UA2 (1982)...at a hadron collider s = 540 GeV m 1,2 = 140 GeV p T,1 = 60 GeV p T,2 = 57 GeV David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

5 Introduction Historical context s = 7 TeV m 1,2 = 3.1 TeV, p T,1 = 520 GeV, p T,2 = 460 GeV David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35 Entering a new era for hadronic final states: ATLAS (20) Our window into the Terascale!

6 Introduction Pile-up and underlying event at the LHC The (busy) environment of a hadron-hadron collision David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

7 Introduction Experimental conditions at the energy frontier Machine conditions during 20 The average number of interactions as inferred from the luminosity measurements as a function of time throughout 20 (assumes σ inel = 71.5 mb) [arxiv: , Submitted to EPJC]. Peak number of interactions, µ ATLAS Preliminary Data 20 Mar 26 Apr 25 May 25 Jun 24 Jul 24 Aug 23 Sep 22 Oct 22 Day in 20 March-June N PV (fraction with N PV 2: <%) June-October N PV (fraction with N PV 2: 40-60%) October+ N PV > 3 (fraction with N PV 2: 0%) David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

8 Jets in ATLAS Roadmap 1 Introduction Historical context Pile-up and underlying event at the LHC Experimental conditions at the energy frontier 2 Jets in ATLAS Jet finding, reconstruction in ATLAS JES uncertainty JES pile-up uncertainty and corrections Jet-vertex association for pile-up filtering 3 Jet physics at ATLAS in 20 Inclusive jet and di-jet cross-section Search for new particles in two-jet final states Jet shapes and corrections for pile-up Jet substructure observables in data New directions 4 Summary and conclusions David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

9 Jets in ATLAS Jet finding, reconstruction in ATLAS Inputs to jet reconstruction ATLAS has a highly flexible and robust set of input signals to consider for jet reconstruction: Towers without noise suppression Topological clusters Towers with noise suppression Tracks Each of these has been studied in detail in the data in order to ensure a thorough understanding of the jet reconstruction itself and the signal model being used to form the basis for physics measurements. David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

Jets in ATLAS Jet finding, reconstruction in ATLAS Inputs to jet reconstruction Since the Tevatron and H1, techniques have been used for combining calorimeter cells for noise-suppression using 3-D

10 Jets in ATLAS Jet finding, reconstruction in ATLAS Inputs to jet reconstruction Since the Tevatron and H1, techniques have been used for combining calorimeter cells for noise-suppression using 3-D clustering, or topological clustering 1 Find seed cells above some noise threshold (E cell /σ noise cell N threshold ) 2 Then cluster cells around that in 3-dimensions, successively allowing in more cells For example: 4σ seed, several layers of 2σ cells, last layer of all cells In ATLAS, these clusters can then be used as the basic object of calibration instead of entire jets. David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, 2011 / 35

11 Jets in ATLAS Jet finding, reconstruction in ATLAS Building a noise suppressed calorimeter grid Mapping clustered cells onto (pseudo-)projective towers David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

12 Jets in ATLAS Jet finding, reconstruction in ATLAS jet track T jet EM+JES T Inputs to jet reconstruction: validation Using the very first 400 µb 1, a complete study of the input constituents to jets was performed. Mean number of constituents 120 ATLAS Preliminary anti-k t R=0.6 tower jets MC QCD di-jets Data 20 s = 7 TeV Mean number of constituents 28 ATLAS Preliminary anti-k t R=0.6 cluster jets MC QCD di-jets Data 20 s = 7 TeV Number of jets ATLAS Preliminary anti-k t R=0.6 cluster jets y < 1.9, p > 0.5 GeV MC QCD di-jets Data 20 s = 7 TeV Number of jets / ATLAS Preliminary anti-k t R=0.6 cluster jets y <2.8, p > 20 GeV Data 20 MC QCD di-jets s = 7 TeV jet Jet y jet Jet y Number of matched tracks Width Towers in jets Topo-clusters in jets Tracks in jets Jet width In nearly all cases, slight discrepancies between the data and PYTHIA 6.4 are observed, and are believed to be primarily due to a combination of difference in the modeling of soft processes and the hard jet fragmentation, as well as a potential need for improvements in the jet-energy scale calibration. David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

13 Jets in ATLAS JES uncertainty Jet energy scale uncertainty The JES uncertainty is the single largest uncertainty for any analysis I will present. Fractional JES Systematic Uncertainty Underlying event (Perugia0) ALPGEN, HERWIG 6, JIMMY Additional Dead Material Noise Thresholds JES calibration non-closure anti k t R=0.6, 0.3<! <0.8, PYTHIA 6 2 Fragmentation (MC09-Pro) Shifted Beam Spot Hadronic Shower Model LAr/Tile Absolute EM Scale Total JES Systematic Uncertainty 2 2 ATLAS 3 p [GeV] T Relative JES pile-up uncertainty Data 20, s=7 TeV ATLAS Preliminary AntiK T, R=0.6 0 <! < <! < <! < <! < <! < jet p [GeV] T It is crucial to determine each component systematically and to provide a well-understood uncertainty, over and above a small uncertainty. Extensive work has gone into performing each of these tasks I will focus on the component which is known to change over time, and to become ever more important as the luminosity of the machine increases to its nominal value: the uncertainty due to multiple interactions in same bunch crossing: pile-up. David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

Jets in ATLAS JES pile-up uncertainty and corrections Measuring the effects of pile-up on the jet energy scale As the level of in-time pile-up increases (i.e. the number of simultaneous proton-proton collisions in the same bunch-crossing) the average total energy deposition in the calorimeters also increases.

14 Jets in ATLAS JES pile-up uncertainty and corrections Measuring the effects of pile-up on the jet energy scale As the level of in-time pile-up increases (i.e. the number of simultaneous proton-proton collisions in the same bunch-crossing) the average total energy deposition in the calorimeters also increases. Clusters, cells (Tile, LAr, HEC) energy 2 minimum bias interactions 3 minimum bias interactions minimum bias interactions Fundamental aspects Assumed to be completely independent of the hard-scattering activity Thus, also independent from the hard-scatter jet p T Roughly uniform in φ with respect to the hard-scatter not with respect to the UE! David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

15 Jets in ATLAS JES pile-up uncertainty and corrections Measuring the effects of pile-up on the jet energy scale As the level of in-time pile-up increases (i.e. the number of simultaneous proton-proton collisions in the same bunch-crossing) the average total energy deposition in the calorimeters also increases. Clusters, cells (Tile, LAr, HEC) energy 2 minimum bias interactions 3 minimum bias interactions minimum bias interactions Fundamental aspects Assumed to be completely independent of the hard-scattering activity Thus, also independent from the hard-scatter jet p T Roughly uniform in φ with respect to the hard-scatter not with respect to the UE! David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

16 Jets in ATLAS JES pile-up uncertainty and corrections Measuring the effects of pile-up on the jet energy scale As the level of in-time pile-up increases (i.e. the number of simultaneous proton-proton collisions in the same bunch-crossing) the average total energy deposition in the calorimeters also increases. Clusters, cells (Tile, LAr, HEC) energy 2 minimum bias interactions 3 minimum bias interactions minimum bias interactions Fundamental aspects Assumed to be completely independent of the hard-scattering activity Thus, also independent from the hard-scatter jet p T Roughly uniform in φ with respect to the hard-scatter not with respect to the UE! David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

17 Jets in ATLAS JES pile-up uncertainty and corrections Thinking about pile-up contributions to the jet-energy scale Traditional approach: pile-up contributes an uncorrelated, soft, diffuse background. subtract this contribution on average or using individual event & jet information. E corrected T = E T O(η, L; N PV, A) ( ) O(η, L; N PV, A) = ρ A Must derive the quantity (density) to subtract David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

18 Jets in ATLAS JES pile-up uncertainty and corrections Thinking about pile-up contributions to the jet-energy scale Traditional approach: pile-up contributes an uncorrelated, soft, diffuse background. subtract this contribution on average or using individual event & jet information. E corrected T = E T O(η, L; N PV, A) ( ) O(η, L; N PV, A) = ρ A Must derive the quantity (density) to subtract Measure the average tower energy density in events with different number of interactions (N PV ) David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

19 Jets in ATLAS JES pile-up uncertainty and corrections Thinking about pile-up contributions to the jet-energy scale Traditional approach: pile-up contributes an uncorrelated, soft, diffuse background. subtract this contribution on average or using individual event & jet information. E corrected T = E T O(η, L; N PV, A) ( ) O(η, L; N PV, A) = ρ A Must derive the quantity (density) to subtract Measure the average tower energy density in events with different number of interactions (N PV ) Measure the event-by-event density by looking at the entire event (Salam, et al.) David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

20 Jets in ATLAS JES pile-up uncertainty and corrections Thinking about pile-up contributions to the jet-energy scale Traditional approach: pile-up contributes an uncorrelated, soft, diffuse background. subtract this contribution on average or using individual event & jet information. E corrected T = E T O(η, L; N PV, A) ( ) O(η, L; N PV, A) = ρ A Must derive the quantity (density) to subtract Measure the average tower energy density in events with different number of interactions (N PV ) Measure the event-by-event density by looking at the entire event (Salam, et al.) Use a 2D/3D grid at any energy scale, with any input under study David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

21 Jets in ATLAS JES pile-up uncertainty and corrections Thinking about pile-up contributions to the jet-energy scale Traditional approach: pile-up contributes an uncorrelated, soft, diffuse background. subtract this contribution on average or using individual event & jet information. E corrected T = E T O(η, L; N PV, A) ( ) O(η, L; N PV, A) = ρ A Must derive the quantity (density) to subtract Measure the average tower energy density in events with different number of interactions (N PV ) Measure the event-by-event density by looking at the entire event (Salam, et al.) Use a 2D/3D grid at any energy scale, with any input under study Must estimate the metric (area) with which we perform the subtraction David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

22 Jets in ATLAS JES pile-up uncertainty and corrections Thinking about pile-up contributions to the jet-energy scale Traditional approach: pile-up contributes an uncorrelated, soft, diffuse background. subtract this contribution on average or using individual event & jet information. E corrected T = E T O(η, L; N PV, A) ( ) O(η, L; N PV, A) = ρ A Must derive the quantity (density) to subtract Measure the average tower energy density in events with different number of interactions (N PV ) Measure the event-by-event density by looking at the entire event (Salam, et al.) Use a 2D/3D grid at any energy scale, with any input under study Must estimate the metric (area) with which we perform the subtraction A tower jet [determined jet-by-jet with towers, averaged with clusters] David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

23 Jets in ATLAS JES pile-up uncertainty and corrections Thinking about pile-up contributions to the jet-energy scale Traditional approach: pile-up contributes an uncorrelated, soft, diffuse background. subtract this contribution on average or using individual event & jet information. E corrected T = E T O(η, L; N PV, A) ( ) O(η, L; N PV, A) = ρ A Must derive the quantity (density) to subtract Measure the average tower energy density in events with different number of interactions (N PV ) Measure the event-by-event density by looking at the entire event (Salam, et al.) Use a 2D/3D grid at any energy scale, with any input under study Must estimate the metric (area) with which we perform the subtraction A tower jet A ghost jet [determined jet-by-jet with towers, averaged with clusters] [theoretically dynamic, but near δ-function for anti-k t jets] David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

24 Jets in ATLAS JES pile-up uncertainty and corrections Thinking about pile-up contributions to the jet-energy scale Traditional approach: pile-up contributes an uncorrelated, soft, diffuse background. subtract this contribution on average or using individual event & jet information. E corrected T = E T O(η, L; N PV, A) ( ) O(η, L; N PV, A) = ρ A Must derive the quantity (density) to subtract Measure the average tower energy density in events with different number of interactions (N PV ) Measure the event-by-event density by looking at the entire event (Salam, et al.) Use a 2D/3D grid at any energy scale, with any input under study Must estimate the metric (area) with which we perform the subtraction A tower jet A ghost jet [determined jet-by-jet with towers, averaged with clusters] [theoretically dynamic, but near δ-function for anti-k t jets] David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

25 Jets in ATLAS JES pile-up uncertainty and corrections Thinking about pile-up contributions to the jet-energy scale Traditional approach: pile-up contributes an uncorrelated, soft, diffuse background. subtract this contribution on average or using individual event & jet information. E corrected T = E T O(η, L; N PV, A) ( ) O(η, L; N PV, A) = ρ A Must derive the quantity (density) to subtract Measure the average tower energy density in events with different number of interactions (N PV ) Measure the event-by-event density by looking at the entire event (Salam, et al.) Use a 2D/3D grid at any energy scale, with any input under study Must estimate the metric (area) with which we perform the subtraction A tower jet A ghost jet [determined jet-by-jet with towers, averaged with clusters] [theoretically dynamic, but near δ-function for anti-k t jets] The point of the offset correction is to render the jet calibration independent of the instantaneous luminosity (pile-up). David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

26 Jets in ATLAS JES pile-up uncertainty and corrections Validation using track-jets at s = 7 TeV [GeV] jet,em T Landau+Gauss fit (MPV) jet p ATLAS Preliminary Data < p T,track-jet 25 < p T,track-jet 30 < p T,track-jet 35 < p T,track-jet 40 < p T,track-jet 45 < p T,track-jet < 25 GeV < 30 GeV < 35 GeV < 40 GeV < 45 GeV < 50 GeV Number of primary vertices (N ) PV Topo-cluster jet p EM T vs N PV (MPV from fit) [GeV] calo T Landau+Gauss fit (MPV) jet p ATLAS Preliminary Data Number of primary vertices (N ) PV Jet-level closure test (MPV from fit) Validation method applied to topo-cluster jets (at the EM scale) Pick discrete p track jet T bins and measure the average calorimeter jet p jet T Slope agrees well with E = 0.5 GeV / vertex systematic due to area average + slight undercorrection David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

Jets in ATLAS Jet-vertex association for pile-up filtering Jet-vertex association Originally developed by the DØ Collaboration The Jet Vertex Fraction (JVF) Associate jets to primary vertices using

$Improve jet-energy reconstruction, missing E t resolution and primary vertex (PV) selection using this jet-vertex fraction, or JVF.$

27 Jets in ATLAS Jet-vertex association for pile-up filtering Jet-vertex association Originally developed by the DØ Collaboration The Jet Vertex Fraction (JVF) Associate jets to primary vertices using tracks and obtain a jet-by-jet energy correction and jet-selection criterion. Improve jet-energy reconstruction, missing E t resolution and primary vertex (PV) selection using this jet-vertex fraction, or JVF. JVF measures the fraction of charged particle transverse momentum in each jet (in the form of tracks) originating in each identified primary vertex in the event. JVF[jet2, PV1] = 0 JVF[jet2, PV2] = 1 jet2 jet1 JVF discriminant definition PV2 PV1 JVF[jet1, PV1] = 1 f JVF[jet1, PV2] = f Z JVF(jet i, vtx j ) = k p T(trk jet i k, vtx j ) l p T(trk jet i l, vtx n ) n For jet i, JVF is the track p T fraction from vertex j. David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

28 Jets in ATLAS Jet-vertex association for pile-up filtering Jet-vertex association and JVF with simulated data -15% of hard-scatter jets (with tracks) have between 1-40% of their energy from MB interactions. ATLAS work in progress April 2009 APS Meeting Fraction of total jets η <2.0, p >20 GeV T Jets without Pure hard-scatter jets Jets with some contribution from pile-up collisions matched tracks 0.3 Jets from 0.2 pile-up collisions ATLAS work in progress Jet-vertex fraction (JVF) JVF distribution for QCD di-jets at L = 33 cm 2 s 1, N MB =2.3 JVF = 1 : Pure hard-scatter jets 0 < JVF < 1: MB spoiled hard-scatter jets JVF = 0 : MB jets JVF = 1 : Jets with no matched tracks QCD di-jets at L = 33 cm 2 s 1, N MB =2.3 94% jets with JVF > 0.75 Cone jets (R=0.4) built from topological clusters Incremental JVF cuts illustrate pile-up contamination David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

29 Jets in ATLAS Jet-vertex association for pile-up filtering Jet selection using JVF As instantaneous luminosity increases, pile-up backgrounds increase while rare, hard-scatter processes remain constant. Jet multiplicity due to the hard scatter interaction should remain flat as a function of luminosity. Average jet multiplicity All jets JVF > 0.75 ATLAS work in progress >=6 Reconstructed vertex multiplicity L = 33 cm 2 s 1, N MB =2.3 (tt) Average jet multiplicity 16 All jets JVF > 0.75 ATLAS work in progress >=8 Reconstructed vertex multiplicity L = 2 33 cm 2 s 1, N MB =4.6 (tt) Using JVF, we can recover the expected flat jet-multiplicity distribution vs. # additional interactions increases without raising the p T threshold. David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

30 Jets in ATLAS Jet-vertex association for pile-up filtering Jet selection using JVF As instantaneous luminosity increases, pile-up backgrounds increase while rare, hard-scatter processes remain constant. Jet multiplicity due to the hard scatter interaction should remain flat as a function of luminosity. Average jet multiplicity All jets JVF > 0.75 ATLAS work in progress >=6 Reconstructed vertex multiplicity L = 33 cm 2 s 1, N MB =2.3 (tt) Average jet multiplicity 16 All jets JVF > 0.75 ATLAS work in progress >=8 Reconstructed vertex multiplicity L = 2 33 cm 2 s 1, N MB =4.6 (tt) However, due to low efficiency for a simple JVF cut (previous slide) jet-energy correction strategies for jets with pile-up contributions are necessary. David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

31 Jet physics at ATLAS in 20 Roadmap 1 Introduction Historical context Pile-up and underlying event at the LHC Experimental conditions at the energy frontier 2 Jets in ATLAS Jet finding, reconstruction in ATLAS JES uncertainty JES pile-up uncertainty and corrections Jet-vertex association for pile-up filtering 3 Jet physics at ATLAS in 20 Inclusive jet and di-jet cross-section Search for new particles in two-jet final states Jet shapes and corrections for pile-up Jet substructure observables in data New directions 4 Summary and conclusions David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

32 Jet physics at ATLAS in 20 First jet physics measurements in ATLAS The program of both standard model measurements and the search for new physics has begun in earnest. I will discuss several important measurements that are not only important in their own right, but also highlight the importance and lasting value of the issues I have been discussing: The inclusive jet cross-section measurement Jet shapes at high p T Search for excited quarks in two-jet events Commissioning jet substructure observables Of course, these are only a tiny fraction of the physics program currently underway, and I refer you to the ATLAS Physics Results pages for a full breakdown of the current public analysis results. David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

33 Jet physics at ATLAS in 20 Inclusive jet and di-jet cross-section Inclusive jet and di-jet cross-sections Single inclusive jet cross-section vs. jet p T for anti-k t, R = 0.6 [pb/gev] dy T!/dp 2 d " 19 L ATLAS -3 jets, R=0.6 anti-k t -1 dt=17 nb, s=7 TeV Systematic Uncertainties NLO pqcd (CTEQ 6.6) Non-pert. corr. 12 y < 0.3 ( ) < y < 0.8 ( ) < y < 1.2 ( ) < y < 2.1 ( ) < y < 2.8 ( ) p [GeV] T NLO pqcd calculated with NLOJET++ Theoretical uncertainties vary µ R and µ F within factor 2, include hadronization and underlying event. Approximately 5%, except at low p T (15% for R = 0.6, % R = 0.4, in appendix) Comparison of cone sizes helps to assess non-perturbative corrections Jet energy scale (JES) uncertainty dominates in all cases Data and theory consistent in all p T and y ranges Double differential cross-section vs. p T David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

34 Jet physics at ATLAS in 20 Inclusive jet and di-jet cross-section Inclusive jet and di-jet cross-sections Di-jet inclusive cross-section vs. jet m 12 for anti-k t, R = 0.6 [pb/gev] max!/dm 12 d y 2 d ATLAS anti-k t jets, R = s = 7 TeV, " L dt = 17 nb 2 2 Systematic uncertainties NLO pqcd (CTEQ 6.6) Non-pert. corr < y < 2.8 ( ) max < y < 2.1 ( ) max < y < 1.2 ( ) max < y < 0.8 ( ) max 0 y < 0.3 ( ) max [GeV] m 12 m 12 is invariant mass of two leading jets p T,1 > 60 GeV p T,2 > 30 GeV y max max( y 1, y 2 ) Observe di-jet masses beyond 2 TeV With this measurement, we begin to see the benefits of extending the energy reach of previous accelerators. However, we pay a price to be at the energy frontier... Double differential di-jet cross-section vs. m 12 David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

35 Jet physics at ATLAS in 20 Search for new particles in two-jet final states Search for excited quarks Events B (D - B) / Data Fit q*(600) q*(900) q*(1500) ATLAS Preliminary s = 7 TeV -1! L dt = 3.1 pb jj Reconstructed m [GeV] The di-jet final state offers a rich arena in which to search for new physics Excited quarks, extra dimensions, additional strong dynamics A bump hunting technique is used: look for a narrow resonance signal, and if none is found, set a limit on Acceptance (A) σ. p T,1 > 80 GeV, p T,2 > 30 GeV and η < 2.5, η < 1.3 Fit a 4-parameter function to the data for the background model Perform a simultaneous 5-parameter fit to the signal and background when extracting the limit PRL 5, , ATLAS-CONF ijet mass distribution (filled points) fitted using a binned background (B) dis- David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

36 Jet physics at ATLAS in 20 Search for new particles in two-jet final states Search for excited quarks # " A [pb] q* MC09 q* Perugia0 q* MC09 Observed 95% CL upper limit Expected 95% CL upper limit Expected limit 68% and 95% bands! -1 L dt = 3.1 pb s = 7 TeV Use several PYTHIA tunes, each utilizing LO theory cross-sections Bayesian prior Excluded at 95%C.L. with MC09 (MRST): 0.30 < m q < 1.53 TeV This is to be compared with m q < 870 GeV, with 1.1 fb 1 from CDF. PRD 79 (2009) 1 ATLAS Preliminary Resonance Mass [GeV] In doing so, we have opened up a new regime in the search for new massive objects. As our reach extends, we will become sensitive phase space for which the decay products of such massive objects merge... ATLAS-CONF pper limit on # A as a function of dijet resonance mass (black filled circles). David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

37 Jet physics at ATLAS in 20 Jet shapes and corrections for pile-up Jet shapes at high p T arxiv: , accepted by Phys. Rev. D! (r) 1 ATLAS (a)! (r) 1 ATLAS -1-1 Data " L dt = 0.7 nb - 3 pb PYTHIA-Perugia20 HERWIG++ (c) ALPGEN PYTHIA-MC09-1 anti-k t jets R = GeV < p < 400 GeV T y < anti-k t jets R = GeV < p < 600 GeV T y < 2.8 DATA / MC r DATA / MC r 3 < p T < 400 GeV (N PV =1) 500 < p T < 600 GeV (N PV =1) Jet shapes represent one of most important handles we have on establishing the description of jets in the data by the Monte Carlo, and is also the first component of jet substructure. We observe that our MC systematically underestimates the width of the jet predicts that the jet mass will be underestimated to some extent as well. Dominant systematic uncertainty is the cluster-level energy scale David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

38 Jet physics at ATLAS in 20 Jet shapes and corrections for pile-up Correcting the jet shape for pile-up ρ a (r) / Ψ(0.7) Ratio Data, One Vertex Events Data, Two Vertex Events Data, Two Vertex Events, Corr. 1.2< y <2.1, 60<p <80 GeV T ATLAS Preliminary Data R = η + φ ρ a (r) / Ψ(0.7) Ratio ATLAS Preliminary Data 20 Data, One Vertex Events Data, Three Vertex Events Data, Three Vertex Events, Corr. 1.2< y <2.1, 60<p <80 GeV T R = η + φ N PV = 1 and 2 (1.2 < y jet < 2.1) N PV = 1 and 3 (1.2 < y jet < 2.1) In order to measure the jet shape in the future, we must be able to reduce the effect of pile-up Potential solution: subtract the measured energy density (from the tower grid) from the jet in annuli Preliminary result: not too bad! Exploring the possibility of full substructure corrections as well. David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

39 Jet physics at ATLAS in 20 Jet substructure observables in data Jet mass and instrumental effects Jet mass [GeV] Data / MC ATLAS Preliminary anti-k t R=1.0 LC cluster jets: η <1.0 MC QCD Jets Data Ldt = 13.8 pb -1 Data Ldt = 13.8 pb Data ( JVF > 0.99) -1 (N Jet p = 1) PV reco T [GeV] reco Jet p [GeV] anti-k t, R = 1.0, p T,1 > 250 GeV, η < 1 T In the context of the LHC, it is crucial to determine instrumental effects on complex observables. Prior to pile-up filtering, jets are observed to have a mean jet mass approximately % higher than events with only a single primary vertex (N PV = 1) for PYTHIA 6. After JVF selection, the agreement with events having no pile-up is within a few %. Punchline: Even very massive jets can have important contributions due to pile-up that must be mitigated. We have developed techniques to do so. David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

40 Jet physics at ATLAS in 20 Jet substructure observables in data Correlating the jet mass with tracking information Calorimeter jet mass [GeV] Data / MC ATLAS Preliminary anti-k t R=1.0 LC cluster jets: η <1.0 MC QCD Jets Data Ldt = 13.8 pb -1 Data Ldt = 13.8 pb Data ( JVF > 0.99) -1 (N = 1) PV Track jet mass [GeV] Track jet mass [GeV] anti-k t, R = 1.0, p T,1 > 250 GeV, η < 1 We can utilize the insensitivity of hard-scatter track-jets to pile-up in order to constrain effects on substructure. The calorimeter jet mass is highly correlated with the track-jet mass. Because the track-jets are constructed from only hard-scattering tracks, the impact of pile-up on calorimeter jets can be immediately inferred. In addition, despite the shortcomings of the PYTHIA MC, when measured against the track-jet mass, the agreement is very good. David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

41 Jet physics at ATLAS in 20 Jet substructure observables in data Jet re-clustering and comparisons with tracks ), sub-jet R(sub-jet ATLAS Preliminary anti-k t R=1.0 LC cluster jets: η <1.0 MC QCD Jets -1 Data Ldt = 13.8 pb (N = 1) PV -1 Data Ldt = 13.8 pb Data ( JVF > 0.99) Number of jets 250 ATLAS Preliminary anti-k t R=1.0 LC cluster jets: η <1.0 MC QCD Jets -1 Data Ldt = 13.8 pb (N = 1) PV Data Ldt = 13.8 pb Data ( JVF > 0.99) R(track,track 1 ) z = min(p,p )/p T,1 T,2 T,J Anti-k t R = 1.0, sub-jet R Anti-k t R = 1.0, sub-jet z Define sub-jets using the k t algorithm with R = 0.3 Left: R separation of the 2 hardest sub-jets compared to the 2 hardest tracks in the jet. Right: Ratio of the 2 nd sub-jet p T to the the original jet p T (crucial parameter in pruning). Punchline: Observe a relative stability of sub-jet kinematics with respect to pile-up David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

42 Jet physics at ATLAS in 20 New directions Improving and extending existing/new tools There are many directions in which to take these tools in order to perform precise and robust substructure-based analyses for the lifetime of the LHC: Jet-vertex fraction jet-by-jet corrections: Being established in the data now using asymmetry and track-jet validation Can be applied to any jet algorithm or input constituent type Jet area-based UE/pile-up subtraction: Use the per-event energy density instead of N PV to calculate ρ First proposed by Salam, et al. and offers a dynamic approach to pile-up subtraction Issue: experimental aspects (i.e. JES) may become more complicated Jet grooming tools: It is possible to use the grooming tools (as in the ATLAS Higgs analysis) in powerful ways, but but how to set JES after? Can we tune these advanced procedures for pile-up subtraction using JVF? David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

43 Jet physics at ATLAS in 20 New directions Correcting jet energies for pile-up contributions L = 33 cm 2 s 1 and N MB =2.3, derived using PYTHIA di-jets truth / p T reco Jet Response: p T QCD di-jet events ATLAS work in progress Jet JVF(PV) Jet response vs. JVF at L = 33 cm 2 s 1 Response < JVF <= < JVF <= < JVF <= < JVF <= 1.00 ATLAS work in progress reco Jet p [GeV] T Jet-energy response vs. p reco T and JVF (di-jet events) Using JVF to correct jet energies in a luminosity independent manner We can derive a jet-energy correction to correct for pile-up contributions on a per-jet basis. JVF(jet i, vtx j ) = k p T(trk jet i k, vtx j ) l p T(trk jet i l, vtx n ) n R(JVF, pjet T ) pcorr T = p jet T /R(JVF, pjet T ) David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

44 Jet physics at ATLAS in 20 New directions Tuning the pruning parameters using JVF See a significant variation of the jet area even after pruning as a function of the number of particles pointing toward the jet from additional interactions. Generator-level study where we define JVF using the stable charged particles from the hard-scattering and we overlay additional min bias events using the generator. Samples provided by David Krohn. David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

45 Summary and conclusions Roadmap 1 Introduction Historical context Pile-up and underlying event at the LHC Experimental conditions at the energy frontier 2 Jets in ATLAS Jet finding, reconstruction in ATLAS JES uncertainty JES pile-up uncertainty and corrections Jet-vertex association for pile-up filtering 3 Jet physics at ATLAS in 20 Inclusive jet and di-jet cross-section Search for new particles in two-jet final states Jet shapes and corrections for pile-up Jet substructure observables in data New directions 4 Summary and conclusions David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

46 Summary and conclusions Status and future jet physics at ATLAS The physics program is well underway at ATLAS, with many of the first results based on hadronic final states, and many more to come. Jet substructure commissioning is progressing fast, so expect many more public results ready soon. It is already clear that our MC is doing very well at describing these very complex observables. A coherent, combined approach to performing integrated measurements of the hadronic final state drives these pursuits, and techniques have been developed that are suitable to the context provided by the LHC, namely, luminosity! As these tools and approaches are combined with the advanced theoretical concepts, the wealth of information that lies inside of jets at the energy frontier can be mined and extracted in order to shed light on new physics manifested through jets. David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 35

47 Backup slides and additional information Roadmap 5 Backup slides and additional information David W. Miller (Stanford, SLAC) ATLAS Jet Physics Results and Jet Substructure February 2, / 1

QCD Jets at the LHC. Leonard Apanasevich University of Illinois at Chicago. on behalf of the ATLAS and CMS collaborations

QCD Jets at the LHC. Leonard Apanasevich University of Illinois at Chicago. on behalf of the ATLAS and CMS collaborations QCD Jets at the LHC Leonard Apanasevich University of Illinois at Chicago on behalf of the ATLAS and CMS collaborations Outline Physics at the LHC Jet Reconstruction and Performance Clustering Algorithms