Recent progress in defining jets

TL;DR

This paper reviews recent advancements in jet definition algorithms and their optimization for LHC physics, emphasizing new methods, implementation improvements, and the importance of parameter choices for different energy scales.

Contribution

It introduces new jet algorithms and implementations, and demonstrates the importance of choosing optimal parameters for different energy scales at the LHC.

Findings

New algorithms improve jet reconstruction accuracy.

Optimal jet radius depends on the energy scale (R~0.5 at 100 GeV, R~1 at TeV).

Results hold even with pileup when proper subtraction is applied.

Abstract

From dedicated QCD studies to new physics background estimation, jets will be everywhere at the LHC. In these proceedings, we discuss two important recent series of improvements. In the first one, we introduce new algorithms and new implementations of previously existing algorithms, in order to cure limitations of their predecessors and to satisfy fundamental requirements. In the second part, we show that it is of prime importance to carefully choose the jet definition (algorithm and parameters) to optimise kinematic reconstructions at the LHC. Noticeably, we show that while at scales around 100 GeV, R~0.5 is an appropriate choice, clustering at the TeV scale requires R~1 for optimal efficiency. We finally show that our results are valid in the presence of pileup, provided that a subtraction procedure is applied.