Pileup Per Particle Identification

TL;DR

The paper introduces PUPPI, a novel particle-level pileup mitigation method that assigns weights to particles based on local shape analysis, improving jet and missing energy measurements in collider experiments.

Contribution

It presents a new particle-based pileup mitigation technique that surpasses existing methods by using local shape variables and flexible combination with other particle information.

Findings

Improves jet $p_T$ and jet mass resolution.

Enhances accuracy of missing transverse energy.

Flexible integration with experimental data.

Abstract

We propose a new method for pileup mitigation by implementing "pileup per particle identification" (PUPPI). For each particle we first define a local shape $\alpha$ which probes the collinear versus soft diffuse structure in the neighborhood of the particle. The former is indicative of particles originating from the hard scatter and the latter of particles originating from pileup interactions. The distribution of $\alpha$ for charged pileup, assumed as a proxy for all pileup, is used on an event-by-event basis to calculate a weight for each particle. The weights describe the degree to which particles are pileup-like and are used to rescale their four-momenta, superseding the need for jet-based corrections. Furthermore, the algorithm flexibly allows combination with other, possibly experimental, probabilistic information associated with particles such as vertexing and timing performance. We demonstrate the algorithm improves over existing methods by looking at jet $p_T$ and jet mass. We also find an improvement on non-jet quantities like missing transverse energy.