Spectral Clustering for Jet Physics

TL;DR

This paper introduces a spectral clustering method for jet physics that directly uses kinematic data, offering a parameter-free alternative to traditional clustering algorithms like anti-k_T, with comparable or improved performance.

Contribution

The paper presents a novel spectral clustering approach for jet definition that is IR-safe and does not require parameter tuning for different physics processes.

Findings

Spectral clustering achieves comparable jet reconstruction performance to anti-k_T.

The method is IR-safe and parameter-free across various physics processes.

Spectral clustering simplifies jet analysis by eliminating the need for parameter adjustments.

Abstract

We present a new approach to jet definition alternative to clustering methods, such as the anti-$k_T$ scheme, that exploit kinematic data directly. Instead the new method uses kinematic information to represent the particles in a multidimensional space, as in spectral clustering. After confirming its Infra-Red (IR) safety, we compare its performance in analysing $qq\to H_{125\,\text{GeV}} \rightarrow H_{40\,\text{GeV}} H_{40\,\text{GeV}} \rightarrow b \bar{b} b \bar{b}$, $qq\to H_{500\,\text{GeV}} \rightarrow H_{125\,\text{GeV}} H_{125\,\text{GeV}} \rightarrow b \bar{b} b \bar{b}$ and $gg,q\bar q\to t\bar t\to b\bar b W^+W^-\to b\bar b jj \ell\nu_\ell$ events from Monte Carlo (MC) samples, specifically, in reconstructing the relevant final states, to that of the anti-kT algorithm. Finally, we show that the results for spectral clustering are obtained without any change in the parameter settings of the algorithm, unlike the anti-kT case, which requires the cone size to be adjusted to the physics process under study.