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Seeing jets in heavy-ion collisions

High Energy Physics - Phenomenology · Submitted 13 Jan 2011

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The main point we address is the subtraction of the large contamination from the underlying event background.

how it is stated · asserted 0.93 read the record →

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arXiv ID1101.2662 (version 1)
Pith ID2357L2E3, Pith's permanent record of this paper and its review
CitationGregory Soyez. "Seeing jets in heavy-ion collisions." arXiv:1101.2662 (2011). https://pith.science/paper/2357L2E3
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 2,859 input tokens, $0.0001 reported.

What the abstract itself states, as probabilities: claims first 0.02 · states limitation 0.17 · human participants 0.02 · controlled comparison 0.10 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.41 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

In these proceedings, we briefly review how jets can be reconstructed in heavy-ion collisions. The main point we address is the subtraction of the large contamination from the underlying event background. We first present the main ingredients needed to define the jets and perform the background subtraction and then discuss the efficiency of the subtraction for different jet algorithms and background-estimation methods.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 In these proceedings, we briefly review how jets can be reconstructed in heavy-ion collisions. background 0.63 0.24 0.03 asserted 0.55
s1 The main point we address is the subtraction of the large contamination from the underlying event background. contribution 0.56 0.24 0.18 asserted 0.93
s2 We first present the main ingredients needed to define the jets and perform the background subtraction and then discuss the efficiency of the subtraction for different jet algorithms and background-estimation methods. method 0.54 0.73 0.08 asserted 0.60

What kind of paper the classifier reports this is: review 1.00 · evidence from literature 0.74.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

In these proceedings, we briefly review how jets can be reconstructed in heavy-ion collisions. The main point we address is the subtraction of the large contamination from the underlying event background. We first present the main ingredients needed to define the jets and perform the background subtraction and then discuss the efficiency of the subtraction for different jet algorithms and background-estimation methods.

Human review
How the literature uses it

What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

What it cites · 11 sentences without a confident label

Uses the method or tool of 5 papers
Cites as background 6 papers

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