Spectral Clustering for Jet Reconstruction

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 $gg\to H_{125\mathrm{GeV}}\to H_{40\mathrm{GeV}}{H}_{40\mathrm{GeV}}\to b\bar{b}b\bar{b}$, $gg\to H_{500\mathrm{GeV}}\to H_{125\mathrm{GeV}}{H}_{125\mathrm{GeV}}\to b\bar{b}b\bar{b}$ and $gg,q\bar{q}\to t\bar{t}\to b\bar{b}{W}^{+}{W}^{-}\to b\bar{b}jjl\nu l$ events from Monte Carlo (MC) samples, specifically, in reconstructing the relevant final states, to that of the anti-$k_T$ 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-$k_T$ case, which requires the cone size to be adjusted to the physics process under study.