Pileup per particle identification

By D. Bertolini, Philip C. Harris, M. Low, Nhan Tran. 2014

424 citations recorded by the source corpus.

A bstractWe 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, probabilistic 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.

Subfields: Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/c66cd7be4dd0b3ba53194912128000f568bb3d91

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In Nuclear and High Energy Physics

Citations in the corpus

Particle-flow reconstruction and global event description with the CMS detector (2017, neutral): “After the end of Run 1, advanced pileup mitigation techniques have been explored [59, 60].”
Energy flow networks: deep sets for particle jets (2018, background): “[30] but also to PUPPI [124] in that a per-particle correction factor could be applied.”
Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques (2020, methodology): “This PUPPI algorithm operates at the PF candidate level, before any jet clustering is performed.”
The Lund jet plane (2018, methodology): “We therefore work within the assumption that methods for pileup mitigation such as SoftKiller [86], PUPPI [87], constituent subtractor [88] or machine learning approaches [89] can successfully remove (9)The Lund-likelihood approach can be sensibly used with a fixed mass window, simply by discretisin”
Particle-flow reconstruction and global event description with the CMS detector (2017, neutral): “After the end of Run 1, advanced pileup mitigation techniques have been explored [59, 60].”
Performance of missing transverse momentum reconstruction in proton-proton collisions at √s = 13 TeV using the CMS detector (2019, methodology): “The momenta of the neutral particles are then rescaled according to the probability that they originate from the PV deduced from the local shape variable [15], superseding the need for jet-based pileup corrections [16].”
Jet substructure at the Large Hadron Collider (2018, methodology): “One example is the PUPPI [80] algorithm which is extensively used in CMS.”
Pileup mitigation at CMS in 13 TeV data (2020, methodology): “To overcome this limitation, a new technique for PU mitigation, pileup per particle identification (PUPPI) [7], is introduced that operates at the particle level.”
Les Houches 2017: Physics at TeV Colliders Standard Model Working Group Report (2017, background): “In particular, PUPPI [685] and the SoftKiller [683] both provide event-wide pileup mitigation techniques that show good performance in terms of resolution, at the expense of requiring some degree of fine-tuning of their free parameters.”
Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning (2017, methodology): “[546] uses graph neural networks to automate the constituent-based pileup mitigation proposed by the pileup per particle identification (PUPPI) method [579].”
Higgs Physics at the HL-LHC and HE-LHC (2019, methodology): “Significant recent advances in constituent-based pileup mitigation techniques will likely play a key role in maintaining and possibly improving the MET performance [534, 535, 536, 537, 538].”
Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV (2016, methodology): “, with more efficient track-vertex association, must therefore maintain a very low misreconstruction rate for tracks from high-pT jets, or also consider the pT and local environment of the tracks, as done with the pileup per particle ID (PUPPI) method [43].”
Searching for long-lived particles beyond the Standard Model at the Large Hadron Collider (2019, methodology): “6.4.1 Pileup mitigation Recently CMS has mad xcellent progress in the ileup m tigation algorithm, i plementing and further developing the PileUp Per Particle approach (PUPPI) [73], which uses local event properties and tracking information to reduc the ef ect of ileup on the physics observables.”