Fuzzy Jets

TL;DR

This paper introduces fuzzy jets, a new clustering algorithm for high energy physics that uses mixture models and maximum likelihood, providing dynamic jet properties and improved stability under pileup conditions.

Contribution

The paper presents fuzzy jets, a novel clustering method based on mixture models and likelihood maximization, enhancing jet property determination and pileup robustness.

Findings

Fuzzy jets can dynamically determine jet size.

Fuzzy jets provide additional information for jet tagging.

Modified fuzzy jets remain stable under high pileup.

Abstract

Collimated streams of particles produced in high energy physics experiments are organized using clustering algorithms to form jets. To construct jets, the experimental collaborations based at the Large Hadron Collider (LHC) primarily use agglomerative hierarchical clustering schemes known as sequential recombination. We propose a new class of algorithms for clustering jets that use infrared and collinear safe mixture models. These new algorithms, known as fuzzy jets, are clustered using maximum likelihood techniques and can dynamically determine various properties of jets like their size. We show that the fuzzy jet size adds additional information to conventional jet tagging variables. Furthermore, we study the impact of pileup and show that with some slight modifications to the algorithm, fuzzy jets can be stable up to high pileup interaction multiplicities.