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jet shapes

jet shapes. Lily Asquith (ANL) on behalf of ATLAS Boost 2012, Valencia. Outline. What are jet shapes, and why are we measuring them? Experimental challenges. The measurements . arxiv:1206.5369 What’s new?. What are jet shapes?.

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jet shapes

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  1. jet shapes Lily Asquith (ANL) on behalf of ATLAS Boost 2012, Valencia

  2. Outline • What are jet shapes, and why are we measuring them? • Experimental challenges. • The measurements. arxiv:1206.5369 • What’s new?

  3. What are jet shapes? Traditionally jet shapes are differential and integrated. arxiv:1101.0070, arxiv:1204.3170 These ‘shapes’ are different measures of energy flow: mass, width, planar flow, eccentricity and angularity. All of these observables are constructed using the angular separation and energy of the jet constituents. e.g. mass: φ A jet. A constituent. η

  4. Width Core-heavy jet: width0 φ η

  5. Width Broad jet: width1 φ η

  6. Quark/ gluon? Quark/gluon jets: width (or girth); gluon jets are broader than quark jets, with more tracks. arXiv:1106.3076v2

  7. Planar flow Two-body jet: Linear energy deposition: Planar flow0 φ η

  8. Planar flow Three-body jet: Planar energy deposition: Planar flow1 φ η

  9. Eccentricity Isotropic energy deposition: eccentricity0 φ η

  10. Eccentricity Elongated energy deposition: eccentricity1 φ η

  11. Two- and higher-body decays Planar flow can distinguish between three-body (top) jets and two-body (light quark/ gluon) jets. arXiv:0807.0234

  12. Angularity τ-2 Asymmetric energy deposition: τ-2maximum φ η

  13. Angularity τ-2 Symmetric energy deposition: τ-20 φ η

  14. Different two-body decays Angularities can distinguish between two-body (W/Z/H) jets with different polarisation and two-body (light quark/gluon) jets. z=m/pT Longitudinal Z jets Transverse Z/ QCD Longitudinal Z/ QCD QCD (light quark, gluon) jets arXiv:0807.0234

  15. Correlations between observables High pT, central, Pythia6 dijets. Mass and width are strongly correlated. Planar flow and eccentricity are strongly anti-correlated.

  16. Correlations between observables No mass cut Mass > 100 GeV At high mass, the correlations change. These are for QCD.

  17. The experimental challenges: aka Pileup

  18. Why pileup is such a problem for jet shapes and substructure 1: These jets are big. These sorts of observables generally change under pileup like R2 or more…

  19. Why pileup is such a problem for jet shapes and substructure 2: We want to be able to distinguish A from B… A B

  20. Why pileup is such a problem for jet shapes and substructure 2: We want to be able to distinguish A from B A B … in these conditions.

  21. Pileup The Number of reconstructed Primary Vertices - NPV – can tell us how much additional radiation we are dealing with. 2010: <NPV>~2 (28% of events NPV=1)  special dataset 2011: <NPV> ~ 10 2012*: <NPV> ~ 25+. 2012

  22. Controlling pileup • Complementary cone technique (CDF)looks in region transverse (in azimuth) to the jet. • Energy deposits in this region are attributed to pileup and underlying event (UE): soft radiation that is always present. arxiv:1101.3002, 1106.5952v2

  23. Controlling pileup • Single vertex events contain only the UE contribution characterisepileup by comparing events with single and multiple vertices. arxiv:1206.5369 measured expected • Can then find the scaling of e.g. ΔM with R obtain subtractions for R=1 jets.

  24. Controlling pileup Complementary cone technique restores distributions to shape seen in single vertex events.

  25. The measurements

  26. Details • Events are selected based on run conditions, data quality and detector conditions. • The anti-kTalgorithm is used with locally calibrated topological clusters as input. • The highest pT jet in each event is measured, must have pT>300 GeV.

  27. Jet mass HERWIG++ 2.4.2, 2.5.1 POWHEG, PYTHIA6 PYTHIA8, PYTHIA6 R=0.6 R=1.0

  28. Jet mass Herwig++ 2.5.1 jet mass prediction is greatly improved w.r.t 2.4.2

  29. Jet mass Eikonalapprox of QCD for gluons and quarks is compatible with our expectation that the data is a mixture of quark and gluon initiated jets.

  30. Jet mass Dominant contributions to the systematic uncertainty are the cluster energy scaleand Monte Carlo predictions. • These show ΔC on the y-axis: • C is the correction factor in bin i when going from detector-level to particle-level • jets in the “baseline” Pythia6 (AMBT1) MC sample. • ΔC is the difference when we vary the sample w.r.t this baseline. • Shading is statistical uncertainty.

  31. Jet width Width is well-modeled by all MCs beyond the first bin.

  32. Details • Planar flow is measured for jets with mass in a window around the top mass. • Not many R=0.6 jets have such a high mass: • Only measure P for R=1.0 jets. • Only measure P in pileup-free (NPV=1) events.

  33. Planar flow Again we see Herwig++ 2.5.1 providing a superior description of the energy flow wrt 2.4.2. Note: this is not the same mass range as the eccentricity measurements.

  34. Details • Eccentricity is measured in the general “region of interest” for boosted particle searches: M>100 GeV.

  35. Eccentricity Eccentricity is a magnifying glass for differences in the distributions of constituents on the “local” angular scale:

  36. Eccentricity Eccentricity is a magnifying glass for differences in the distributions of constituents on the “local” angular scale: This piece varies significantly between MCs, but (mostly) washes away with energy weight (soft particles). Highly anti-correlated with planar flow (-90% for jets in same high mass range)

  37. Details • QCD small-angle approximation gives a prediction for the peak and maximum values of the τ-2 distribution: • Valid for “fixed” high mass and pT (we choose 100<M<130) • Meaningful for “smaller” jets only • Corrections in 2010 pileup conditions are negligible, so none applied.

  38. Angularity Nice agreement between data and MC and with QCD small angle approx.

  39. What’s new?

  40. Jet mass and 2011 pileup The jet mass versus the number of reconstructed primary vertices per event (NPV) in 2011 data for five different jet algorithm/pruning configurations. From left to right these are [1] Anti-kt, [2] Pruned anti-kt, [3] Trimmed anti-kt, [4] Cambridge-Aachen and [5] Filtered Cambridge-Aachen. As the animation plays, the distance parameter (R) of the jet increases from 0.4 to 1.6. The mean mass in each bin of NPV is indicated by the black markers

  41. Angularity and 2011 pileup The jet angularity versus the number of reconstructed primary vertices per event (NPV) in 2011 data for five different jet algorithm/pruning configurations. From left to right these are [1] Anti-kt, [2] Pruned anti-kt, [3] Trimmed anti-kt, [4] Cambridge-Aachen and [5] Filtered Cambridge-Aachen. The mean mass in each bin of NPV is indicated by the black markers.

  42. In summary • Our current MC generators are correctly describing the jet substructure we see in data, in some detail. • 2011 and 2012 data: • More data! • More opportunity to explore methods for dealing with pileup! • More opportunity to ask questions about how the characteristics of a jet vary according to its parenthood!

  43. Details of the grooming configurations • Pruned: During the reclustering of the jet we look at the pT fraction and angle of the cluster we are seeking to combine into our protojet. If the cluster is soft, i.e. carries less than 6% of the protojetpT, or is "wide angle" dR>0.3, then we chuck it. Then we rebuild the jet from remaining clusters. • Trimmed: The jet constituents are reclustered with a small distance parameter R=0.3 into subjets. Any subjet with pT<5% of the jet pT is chucked. Then we rebuild the jet from remaining clusters. • Filtered: The jet constituents are reclustered with a small distance parameter R=0.3 into subjets. Anything outside the three hardest subjets is chucked. Then we rebuild the jet from remaining clusters.

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