Spectral clustering for jet physics

Srinandan Dasmahapatra

Abstract:

We present a new approach to jet definition alternative to clustering methods,
such as the anti-kT 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
\(gg\to H_{125\,\text{GeV}} \rightarrow H_{40\,\text{GeV}} H_{40\,\text{GeV}} \rightarrow b \bar{b} b \bar{b}\),
\(gg\to H_{500\,\text{GeV}} \rightarrow H_{125\,\text{GeV}} H_{125\,\text{GeV}} \rightarrow b \bar{b} b \bar{b}\)
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 \antikt{} algorithm.
Finally, we show that the results for spectral clustering are obtained without any change in the parameter settings of the algorithm,
unlike the \antikt{} case, which requires the cone size to be adjusted to the physics process under study.