Run II Jet Physics: Proceedings of the Run II QCD and Weak Boson Physics Workshop

TL;DR

This paper reviews and recommends standardized jet algorithms for Run II collider experiments, focusing on cone and KT algorithms, to improve consistency and accuracy in jet measurements and energy flow analysis.

Contribution

It introduces unified Run II jet algorithms for CDF and DØ, addressing calibration, seed minimization, and preclustering issues, building on Run I experience.

Findings

Recommended same algorithms for CDF and DØ

Discussed calibration and algorithm performance

Provided preliminary experimental results

Abstract

The Run II jet physics group includes the Jet Algorithms, Jet Shape/Energy Flow, and Jet Measurements/Correlations subgroups. The main goal of the jet algorithm subgroup was to explore and define standard Run II jet finding procedures for CDF and DO. The focus of the jet shape/energy flow group was the study of jets as objects and the energy flows around these objects. The jet measurements/correlations subgroup discussed measurements at different beam energies; strong coupling constant measurements; and LO, NLO, NNLO, and threshold jet calculations. As a practical matter the algorithm and shape/energy flow groups merged to concentrate on the development of Run II jet algorithms that are both free of theoretical and experimental difficulties and able to reproduce Run I measurements. Starting from a review of the experience gained during Run I, the group considered a variety of cone algorithms and KT algorithms. The current understanding of both types of algorithms, including calibration issues, are discussed in this report along with some preliminary experimental results. The jet algorithms group recommends that CDF and DO employ the same version of both a cone algorithm and a KT algorithm during Run II. Proposed versions of each type of algorithm are discussed. The group also recommends the use of full 4-vector kinematic variables whenever possible. The recommended algorithms attempt to minimize the impact of seeds in the case of the cone algorithm and preclustering in the case of the KT algorithm. Issues regarding precluster definitions and merge/split criteria require further study.