# The anti-k_t jet clustering algorithm

**Authors:** Matteo Cacciari, Gavin P. Salam, Gregory Soyez  
**arXiv:** [0802.1189](https://arxiv.org/abs/0802.1189)  
**Date:** 2008-02-08  
**Summary page:** https://www.summarizepaper.com/en/paper/0802.1189v1/

## Key points

- The paper explores the properties of the "anti-k_t" jet clustering algorithm
- The algorithm uses a negative power in the energy scale distance measure
- It behaves similarly to an idealized cone algorithm with equal active and passive areas for jets with soft fragmentation forming conical shapes
- Unique characteristics include zero area anomalous dimensions, non-global logarithms resembling those of a rigid boundary, and a universal Milan factor
- The anti-k_t algorithm could potentially replace the iterative cone algorithm due to its fast performance and enhanced infrared and collinear safety

## Summary

The paper "The Anti-k_t Jet Clustering Algorithm" by Matteo Cacciari, Gavin P. Salam, and Gregory Soyez explores the properties of a new member in the class of sequential recombination jet algorithms. This algorithm, known as the "anti-k_t" algorithm, utilizes a negative power in the energy scale distance measure. The study reveals that this algorithm exhibits behavior similar to an idealized cone algorithm with equal active and passive areas for jets with soft fragmentation forming conical shapes. Additionally, it demonstrates unique characteristics not found in existing algorithms like SISCone or cone algorithms with split-merge steps. These include zero area anomalous dimensions, non-global logarithms resembling those of a rigid boundary, and a universal Milan factor. Interestingly, these properties align closely with those of the collinear unsafe plain "iterative cone" algorithm. The findings suggest that the anti-k_t algorithm could serve as a natural and efficient replacement for the iterative cone algorithm due to its fast performance and enhanced infrared and collinear safety. Overall, this research sheds light on a novel approach to jet clustering in hadron-hadron collisions and offers insights into potential advancements in particle physics analysis techniques.