Developments with the Cone Algorithm in Run II

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Developments with the Cone Algorithm in Run II John Krane Iowa State University MC Workshop Oct. 4 2002, Fermilab Part I: Data vs MC, interpreted as physics Part II: Data vs MC, interpreted as a tuning problem

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Developments with the Cone Algorithm in Run II. John Krane Iowa State University. Part I: Data vs MC, interpreted as physics Part II: Data vs MC, interpreted as a tuning problem. MC Workshop Oct. 4 2002, Fermilab. Lost Jets and Search Cones. CDF: Matthais Toennesmann DØ: John Krane - PowerPoint PPT Presentation

Transcript of Developments with the Cone Algorithm in Run II

Page 1: Developments with the Cone Algorithm in Run II

Developments with the Cone Algorithm in Run II

John KraneIowa State University

MC Workshop Oct. 4 2002, Fermilab

Part I: Data vs MC, interpreted as physics

Part II:Data vs MC, interpretedas a tuning problem

Page 2: Developments with the Cone Algorithm in Run II

John Krane -- DØ 2

Lost Jets and Search Cones

CDF: Matthais Toennesmann

DØ: John Krane

Cones can iteratate away from “small”Energy clusters

There is a reason I’m showing the CDF image

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John Krane -- DØ 3

Best Description of Procedure Use a small cone to find jets and iterate locations Expand cone size to full 0.7 and save Find midpoints Iterate 0.7 size midpoint jets

Wanted to check CDF’s solution and provide feedback

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John Krane -- DØ 4

Results on selected sample (45 evts)

Seed tracking on a sample of 45 suspicious events

Distance of nearestfound jet from original seed

Symmetric in y-,so just use R...

Abs y drift

Abs

d

rift Each point was a seed

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John Krane -- DØ 5

Drift distance for 0.7 cones, pT>15 GeV

If a seed is too close (R/2) to existing jet, ignore it

Standard cones can drift very long distances!

Search cone R/2 limits drift to R

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John Krane -- DØ 6

R=0.5 Cones

Same comments apply...

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John Krane -- DØ 7

R=0.3 cones

Again...

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John Krane -- DØ 8

Normalize drift distances by R

R=0.5 cones,scaled distance

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John Krane -- DØ 9

x-axes have suppressed zero

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John Krane -- DØ 10

CPU Requirements

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John Krane -- DØ 11

Conclusions for Search Cones

Cones can drift quite far from the seed, even for reasonably high-pT Jets >15 GeV

This doesn’t mean a jet is “lost” every time this happens (I have yet to find a lost jet in DØ data)

Search cones can limit drift as much as we like

R/2 works well (almost perfectly) R

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John Krane -- DØ 12

Suggestions for Future Work

Run full Reco tests for CPU time and consistency

Consult CDF and try to converge on a parameter – Informally, Joey Huston thinks R/2 works well– Would like permission to show this talk externally

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John Krane -- DØ 13

Inclusive Jet and Dijet Mass

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John Krane -- DØ 14

Integrated Luminosity for Moriond

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John Krane -- DØ 15

Current Jet Triggers

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Future Jet Triggers

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John Krane -- DØ 17