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Ondas gravitacionais de buracos negros coalescentes: um estudo quantitativo a partir de física básica

2022· article· pt· W4285143254 on OpenAlexaff
Nicolas L.N.S. Nascimento, R. R. Cuzinatto

Bibliographic record

VenueRevista Brasileira de Ensino de Física · 2022
Typearticle
Languagept
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHumanitiesLIGOArtPhysicsPhilosophyGravitational waveAstronomy

Abstract

fetched live from OpenAlex

Este trabalho mostra que é possível entender as características essenciais das ondas gravitacionais emitidas pela coalescência de sistemas binários a partir dos conhecimentos de física básica. Empregamos não mais do que conceitos abordados nos cursos introdutórios de mecânica e eletromagnetismo de uma graduação em ciências exatas. Derivamos cuidadosamente as equações necessárias para compreender quantitativamente as conclusões apontadas no artigo de descoberta das ondas gravitacionais da Colaboração LIGO para o evento GW150914, com precisão de ordem de grandeza ou maior. Usamos os dados disponibilizados para a amplitude das ondas gravitacionais, as frequências do início da fase espiral e de chirp e o tempo até a coalescência para estimar, por exemplo, a massa de chirp do par de buracos negros que coalesceram, a massa total do sistema, as massas individuais de cada buraco negro, o tamanho do sistema binário e a distância da Terra aos buracos negros do evento GW150914. Para ilustrar a potencialidade da nossa abordagem, estendemos a nossa análise quantitativa aos dez primeiros eventos divulgados pela Colaboração LIGO-Virgo entre 12 de fevereiro de 2016 (evento de descoberta) e 20 junho de 2020 (evento GW190814).

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

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.

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

Opus teacher head0.026
GPT teacher head0.341
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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