Discriminating signal from background using neural networks. Application to top-quark search at the Fermilab Tevatron

G. Stimpfl-Abele; Ll. Ametller; Ll. Garrido; P. Talavera; P. Yepes

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Discriminating signal from background using neural networks. Application to top-quark search at the Fermilab Tevatron

Ll. Ametller, Ll. Garrido, G. Stimpfl-Abele, P. Talavera, P. Yepes

High Energy Physics - Phenomenology · Submitted 11 Mar 1996

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

The authors' main claim The application of Neural Networks in High Energy Physics to the separation of signal from background events is studied.

how it is stated · asserted 0.84 read the record →

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arXiv IDhep-ph/9603269 (version 1)

Pith IDGKJHY5AH, Pith's permanent record of this paper and its review

CitationG. Stimpfl-Abele, Ll. Ametller, Ll. Garrido, P. Talavera, P. Yepes. "Discriminating signal from background using neural networks. Application to top-quark search at the Fermilab Tevatron." arXiv:hep-ph/9603269 (1996). https://pith.science/paper/GKJHY5AH

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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, 2,851 input tokens, $0.0001 reported.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.12 · human participants 0.02 · controlled comparison 0.07 · randomized assignment 0.01 · states quantitative result 0.03 · compares with prior results 0.08 · mentions code or data availability 0.02.

The abstract, sentence by sentence

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

The application of Neural Networks in High Energy Physics to the separation of signal from background events is studied. A variety of problems usually encountered in this sort of analyses, from variable selection to systematic errors, are presented. The top--quark search is used as an example to illustrate the problems and proposed solutions.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	The application of Neural Networks in High Energy Physics to the separation of signal from background events is studied.	contribution 0.90	0.48	0.06	asserted 0.84
s1	A variety of problems usually encountered in this sort of analyses, from variable selection to systematic errors, are presented.	contribution 0.71	0.44	0.04	asserted 0.94
s2	The top--quark search is used as an example to illustrate the problems and proposed solutions.	method 0.52	0.53	0.07	asserted 0.98

What kind of paper the classifier reports this is: method or tool 0.89 · evidence from simulation or computation 0.53.

What was examined, and what was not

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arXiv listing abstract v1

not examined

full text
figures and tables
references and citing sentences
relations to other papers

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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

The application of Neural Networks in High Energy Physics to the separation of signal from background events is studied. A variety of problems usually encountered in this sort of analyses, from variable selection to systematic errors, are presented. The top--quark search is used as an example to illustrate the problems and proposed solutions.

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1 arXiv paper cite this in 1 sentence

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

Papers citing it

Cites it as background 1 paper

Classification algorithms have been used for this purpose since the 1980s or 1990s, see e.g. [ref] and since 2014 [ref] more and more deep learning algorithms have been used.

Universal Anomaly Detection at the LHC: Transforming Optimal Classifiers and the DDD Method · Introduction · 0.93

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