measurements of jet properties

1. measurements of jet properties Lily Asquith (ANL) 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 and why are we measuring them?

4. Why measure jet shapes? We can use them to distinguish between three-body (top) jets and two-body (light quark/ gluon) jets: planar flow. arXiv:0807.0234

5. Why measure jet shapes? Two-body (W/Z/H) jets with different polarisation and two-body (light quark/gluon) jets: angularities. arXiv:0807.0234

6. Why measure jet shapes? Heavy/light flavour and quark/gluon jets: width (or girth) arXiv:1010.3698v2 arXiv:1106.3076v2

7. 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: planar flow, angularity, width and mass. 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

8. Width Core-heavy jet: width0 φ η

9. Width Broad jet: width1 φ η

10. Planar flow Two-body jet: Linearenergy deposition: Planar flow0 φ η

11. Planar flow Three-body jet: Planarenergy deposition: Planar flow1 φ η

12. Eccentricity Isotropicenergy deposition: eccentricity1 φ η

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

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

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

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

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

18. The experimental challenges: aka Pileup

19. 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…

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

21. 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.

22. Pileup in 2010 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

23. Pileup in 2011 <NPV> ~ 10 .

24. Pileup in 2012 2012*: <NPV> ~ 25+.

25. 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. • Single vertex events contain only the UE contribution characterisepileup by comparing events with single and multiple vertices. • Can then find the scaling of e.g. ΔM with Robtain subtractions for R=1 jets. arxiv:1101.3002, 1106.5952v2 arxiv:1206.5369 measured Scaling: expected

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

27. The measurements

28. 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.

29. 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.

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

31. 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.

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

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

34. 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.

35. 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.

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

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

38. 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)

39. 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.

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

41. What’s new?

42. Jet mass and 2011 pileup Note for practice talk: changes will be made to this slide following approvals 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

43. Jet width and 2011 pileup Note for practice talk: changes will be made to this slide following approvals The jet width 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

44. Angularity and 2011 pileup Note for practice talk: changes will be made to this slide following approvals The jet width 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.

45. In summary • Our current MC generators are correctly describing the jet substructure we see in data, in some detail. • The hard work spent nurturing non-standard jets and obscure measurements is certainly paying off. • 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!