Jet maximization, axis minimization, and stable cone finding
Jet finding is a type of optimization problem, where hadrons from a high-energy collision event are grouped into jets based on a clustering criterion. As three interesting examples, one can form a jet cluster that (i) optimizes the overall jet four-vector, (ii) optimizes the jet axis, or (iii) align...
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Other Authors: | , |
Format: | Article |
Language: | English |
Published: |
American Physical Society,
2015-10-02T12:03:11Z.
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Subjects: | |
Online Access: | Get fulltext |
Summary: | Jet finding is a type of optimization problem, where hadrons from a high-energy collision event are grouped into jets based on a clustering criterion. As three interesting examples, one can form a jet cluster that (i) optimizes the overall jet four-vector, (ii) optimizes the jet axis, or (iii) aligns the jet axis with the jet four-vector. In this paper, we show that these three approaches to jet finding, despite being philosophically quite different, can be regarded as descendants of a mother optimization problem. For the special case of finding a single cone jet of fixed opening angle, the three approaches are genuinely identical when defined appropriately, and the result is a stable cone jet with the largest value of a quantity J. This relationship is only approximate for cone jets in the rapidity-azimuth plane, as used at the Large Hadron Collider, though the differences are mild for small radius jets. United States. Dept. of Energy (Cooperative Research Agreement DE-SC-00012567) United States. Dept. of Energy (Early Career Research Program DE-SC-0006389) Alfred P. Sloan Foundation (Sloan Research Fellowship) |
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