Jet Sampling: Improving Event Reconstruction through Multiple Interpretations

TL;DR

This paper introduces a method to improve event classification in particle physics by considering multiple interpretations of jet events, enhancing analysis accuracy and measurement precision.

Contribution

It generalizes the Qjets approach to event-level analysis, allowing events to have weighted classifications rather than binary, thereby improving discrimination and measurement uncertainties.

Findings

28% improvement in significance for Higgs plus Z analysis

Multiple interpretations enhance event discrimination

Reduces uncertainty in measurements

Abstract

The classification of events involving jets as signal-like or background-like can depend strongly on the jet algorithm used and its parameters. This is partly due to the fact that standard jet algorithms yield a single partition of the particles in an event into jets, even if no particular choice stands out from the others. As an alternative, we propose that one should consider multiple interpretations of each event, generalizing the Qjets procedure to event-level analysis. With multiple interpretations, an event is no longer restricted to either satisfy cuts or not satisfy them - it can be assigned a weight between 0 and 1 based on how well it satisfies the cuts. These cut-weights can then be used to improve the discrimination power of an analysis or reduce the uncertainty on mass or cross-section measurements. For example, using this approach on a Higgs plus Z boson sample, with h->bb we find an 28% improvement in significance can be realized at the 8 TeV LHC. Through a number of other examples, we show various ways in which having multiple interpretations can be useful on the event level.