FastJet: a code for fast k_t clustering, and more

TL;DR

FastJet introduces an efficient algorithm for k_t jet clustering that significantly reduces computational complexity, making it practical for analyzing high-multiplicity events in particle physics.

Contribution

The paper presents a new geometrical algorithm that reduces k_t clustering complexity from N^3 to N log N, enabling faster analysis.

Findings

FastJet is orders of magnitude faster than previous codes.

The algorithm is infrared and collinear safe.

It is suitable for high-multiplicity heavy ion events.

Abstract

Two main classes of jet clustering algorithms, cone and k_t, are briefly discussed. It is argued that the former can be often cumbersome to define and implement, and difficult to analyze in terms of its behaviour with respect to soft and collinear emissions. The latter, on the other hand, enjoys a very simple definition, and can be easily shown to be infrared and collinear safe. Its single potential shortcoming, a computational complexity believed to scale like the number of particles to the cube (N^3), is overcome by introducing a new geometrical algorithm that reduces it to N ln N. A practical implementation of this approach to k_t-clustering, FastJet, is shown to be orders of magnitude faster than all other present codes, opening the way to the use of k_t-clustering even in highly populated heavy ion events.