PAIReD jet: A multi-pronged resonance tagging strategy across all Lorentz boosts

TL;DR

This paper introduces PAIReD jets, a novel multi-pronged resonance tagging method that improves heavy-flavor jet reconstruction and background rejection across all Lorentz boost regimes using machine learning.

Contribution

It presents a new jet reconstruction strategy that leverages clustered small-radius jets and machine learning to enhance heavy-flavor jet identification across all boost regimes.

Findings

Higher signal reconstruction efficiency with PAIReD jets.

Significantly better background rejection at low Lorentz boosts.

Comparable rejection rates to large-radius jets at high boosts.

Abstract

We propose a new approach of jet-based event reconstruction that aims to optimally exploit correlations between the products of a hadronic multi-pronged decay across all Lorentz boost regimes. The new approach utilizes clustered small-radius jets as seeds to define unconventional jets, referred to as PAIReD jets. The constituents of these jets are subsequently used as inputs to machine learning-based algorithms to identify the flavor content of the jet. We demonstrate that this approach achieves higher efficiencies in the reconstruction of signal events containing heavy-flavor jets compared to other event reconstruction strategies at all Lorentz boost regimes. Classifiers trained on PAIReD jets also have significantly better background rejections compared to those based on traditional event reconstruction approaches using small-radius jets at low Lorentz boost regimes. The combined effect of a higher signal reconstruction efficiency and better classification performance results in a two to four times stronger rejection of light-flavor jets compared to conventional strategies at low Lorentz-boosts, and rejection rates similar to classifiers based on large-radius multi-pronged jets at high Lorentz-boost regimes.