K T Inclusive Jet Cross Section

1. KT Inclusive Jet Cross Section Michael Strang University of Texas at Arlington July 11, 2000 DOE Review UT Arlington

2. Outline • Motivation • Implementation • Ntuple Creation • Cut Efficiency • Conclusion

3. Motivation • KT Algorithm • - Faithful attempt to follow the physical process of fragmentation, multiple soft gluon emission, through the iterative reconstruction of “proto-jets” into full hadronic jets • - Theoretically infrared and collinear safe to all orders in perturbative QCD (does not involve arbitrary split/merge decision required if cones overlap) • - Can be applied to vectors from fixed order • or resummed calculations in QCD, partons • or particles from Monte Carlo, or energy • deposited in a calorimeter

4. Implementation • Precluster to redistribute low energy cells amongst nearby cells • Recombination procedure (D free parameter): 1. For each pair of clusters, i and j, define: where For each cluster define: 2. The minimum, dmin, of all di and dij is found • 3. If dmin is a dij then the clusters are merged, using the 4 • vector recombination scheme, into a new cluster k • with: • 4. If dmin is a di then the cluster is “not mergable” and it is • dropped from the cluster list and added to the jet list 5. Repeat 1-5 until all clusters have become jets

5. Ntuple Creation • UTA contribution • Data Set • Run 1b d0fixed mdst • Use D of 1.0 in KT algorithm • + Corresponds to R = 0.7 in cone • algorithm reducing renormalization • and factorization scale dependence • of cross section • Jet triggers: • + JET_MIN, JET_30, JET_50, JET_85 • JET_MAX • - Reduces data to ~770k events • Storage • 55 separate ntuples • + ~25 GB of space • + chained together for analysis • Usage • Used in many continuing Ph.D. theses • + Inclusive Jet Cross Section • + Thrust Distributions • + Subjet Multiplicity in Q and G Jets

6. Cut Efficiency • UTA contribution to Inclusive Jet measurement • Various cuts applied to insure only good jets • enter sample. Efficiency is a measure of how well bad jets are properly rejected while keeping good jets. • - Applied to data before being corrected for energy • Scale Vertex Cut • Revertexing • For events with two vertices, vector sum of ET is • compared and vertex with lowest value is used. This value becomes the missing ET • Cut • Eliminates jets that cannot be accurately • measured in detector • Efficiency • Ratio of number of events that pass to total • number of events • Over entire range: 90.3 ± 0.1 %

7. Cut Efficiency (cont’d) • Missing ET • Two different cuts depending on leading ET • lead ET < 100. GeV: missing ET < 30. GeV • otherwise : missing ET / lead ET < 0.3 • Eliminates poorly measured jets and cuts out • fakes caused by radiation from main ring and some hot cells • Efficiency • Fit f(x) = exp(A+Bx) in the good region that is • well described by this function and extrapolate outside the cut • Ratio of number of events in good region to • number of events in good region plus integral of fit in bad region • For ET > 150. : 98.0 ± 0.1 % • Otherwise: Fit to a quadratic and match

8. Cut Efficiency (cont’d) • EMFrac • Cut: 0.05 < EMFrac < 0.95 • Eliminates fakes or misidentified photons or • electrons • Efficiency • Fit gaussian to each tail in the good region that is • well described by this function and extrapolate outside the cut • Ratio of number of events in good region to • number of events in good region plus integral of fit in bad region • Efficiency: 99.84 ± 0.02% • CHFrac • Cut: CHFrac < 0.40 • Eliminates deposits from main ring • Efficiency • Fit same form as missing ET • Efficiency: 99.88 ± 0.05%

9. Conclusion • KT Algorithm has many nice properties that lend • themselves to QCD studies • Iterative routine easily processed by computer • but too slow to be used in triggering • Ntuple contains KT information for nearly all • events from Run 1b • Cuts reduce data to what is considered to be • good jets that can then be used in the full analysis. Cuts are very efficient. • Analysis is being continued by Sebastian • Grinstein at FNAL