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Bayesian event reconstruction and background rejection in neutrino detectors

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Gary C. Hill

Astrophysics · Submitted 5 Jun 2001

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

The authors' main claim Further, it will be shown how signals can be separated from backgrounds through integration of Bayesian posterior probability densities.

how it is stated · asserted 0.99 read the record →

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arXiv IDastro-ph/0106081 (version 1)
Pith IDXAQ6RTHZ, Pith's permanent record of this paper and its review
CitationGary C. Hill. "Bayesian event reconstruction and background rejection in neutrino detectors." arXiv:astro-ph/0106081 (2001). https://pith.science/paper/XAQ6RTHZ
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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-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,477 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.06 · human participants 0.02 · controlled comparison 0.07 · randomized assignment 0.01 · states quantitative result 0.03 · compares with prior results 0.10 · mentions code or data availability 0.02.

The abstract, sentence by sentence

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

Several large volume, high energy neutrino detectors are in operation or in the design stage. Upward going signal neutrino events must be separated from large backgrounds of downgoing cosmic ray induced atmospheric muons. To this end, a Bayesian extension of the traditional maximum likelihood reconstruction method will be described. Further, it will be shown how signals can be separated from backgrounds through integration of Bayesian posterior probability densities.

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 Several large volume, high energy neutrino detectors are in operation or in the design stage. background 1.00 0.05 0.02 asserted 1.00
s1 Upward going signal neutrino events must be separated from large backgrounds of downgoing cosmic ray induced atmospheric muons. background 1.00 0.07 0.03 asserted 0.99
s2 To this end, a Bayesian extension of the traditional maximum likelihood reconstruction method will be described. method 0.75 0.45 0.10 asserted 0.86
s3 Further, it will be shown how signals can be separated from backgrounds through integration of Bayesian posterior probability densities. method 0.73 0.67 0.35 asserted 0.99

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from argument 0.54.

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

Several large volume, high energy neutrino detectors are in operation or in the design stage. Upward going signal neutrino events must be separated from large backgrounds of downgoing cosmic ray induced atmospheric muons. To this end, a Bayesian extension of the traditional maximum likelihood reconstruction method will be described. Further, it will be shown how signals can be separated from backgrounds through integration of Bayesian posterior probability densities.

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