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Pileup per particle identification

2014· article· en· 373 citations· W2097795946 on OpenAlex· 10.1007/jhep10(2014)059

Why is this work in the frame?

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

Canadian funderA Canadian agency funded it. The work may carry no Canadian affiliation at all.

No Canadian affiliation. An affiliation-only frame — the usual design — would never have seen this work. It is one of the works that make the case for inverting the frame.

Machine scores (provisional)

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Opus teacher head0.009
GPT teacher head0.237
Teacher spread
0.228 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

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, 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

The record

Venue
Journal of High Energy Physics
Topic
Particle physics theoretical and experimental studies
Field
Physics and Astronomy
Canadian institutions
Funders
Natural Sciences and Engineering Research Council of CanadaIstituto Nazionale di Fisica NucleareFermilabCERNU.S. Department of Energy
Keywords
Event (particle physics)Particle (ecology)PhysicsJet (fluid)Probabilistic logicStatistical physicsParticle identificationTransverse planeIdentification (biology)Charged particleComputational physicsComputer scienceMechanicsArtificial intelligenceOpticsAstrophysicsIonGeologyEngineeringQuantum mechanics
Has abstract in OpenAlex
yes