article · 2014
Pileup per particle identification
Published For Sissa Springer, Daniele Bertolini, A Philip Harris, B Matthew Lowc, Nhan Tr
Cited by 0Abstract and details only
Abstract
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 α 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 α 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, probabilis-tic information associated with particles such as vertexing and timing performance. We demonstrate the algorithm improves over existing methods by looking at jet pT and jet mass. We also find an improvement on non-jet quantities like missing transverse energy.
Cite this paper
Springer, P. F. S., Bertolini, D., Harris, A. P., Lowc, B. M., & Tr, N. (2014). Pileup per particle identification.
Springer, Published For Sissa, et al. “Pileup per particle identification.” 2014.
Springer, Published For Sissa, Daniele Bertolini, A Philip Harris, B Matthew Lowc, and Nhan Tr. 2014. “Pileup per particle identification.”
Springer, P.F.S. et al. (2014) ‘Pileup per particle identification’.
@article{springer2014pileup,
title = {{Pileup per particle identification}},
author = {Springer, Published For Sissa and Bertolini, Daniele and Harris, A Philip and Lowc, B Matthew and Tr, Nhan},
year = {2014}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.