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Record W2798847725 · doi:10.1103/physrevd.102.123010

Constraining black hole mimickers with gravitational wave observations

2020· article· en· W2798847725 on OpenAlexafffund
Nathan K. Johnson-McDaniel, Arunava Mukherjee, Rahul Kashyap, P. Ajith, W. Del Pozzo, S. Vitale

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsCanadian Institute for Advanced Research
FundersH2020 Marie Skłodowska-Curie ActionsScience and Technology Facilities CouncilInstitut Périmètre de physique théoriqueScience and Engineering Research BoardIndo-US Science and Technology ForumCalifornia Institute of TechnologyEuropean CommissionIndustry CanadaMassachusetts Institute of TechnologyOntario Ministry of Economic Development and InnovationNational Science FoundationGovernment of CanadaMinistero dello Sviluppo EconomicoInternational Centre for Theoretical SciencesMax-Planck-Gesellschaft
KeywordsLIGOPolytropic processPhysicsBinary black holeBlack hole (networking)Gravitational waveGeneral relativityBinary numberStarsAstrophysicsParameter spaceSimple (philosophy)GravitationTheoretical physicsAstronomyComputer science

Abstract

fetched live from OpenAlex

LIGO and Virgo have recently observed a number of gravitational wave (GW) signals that are fully consistent with being emitted by binary black holes described by general relativity. However, there are theoretical proposals of exotic objects that can be massive and compact enough to be easily confused with black holes. Nevertheless, these objects differ from black holes in having nonzero tidal deformabilities, which can allow one to distinguish binaries containing such objects from binary black holes using GW observations. Using full Bayesian parameter estimation, we investigate the possibility of constraining the parameter space of such ``black hole mimickers'' with upcoming GW observations. Employing perfect fluid stars with a polytropic equation of state as a simple model that can encompass a variety of possible black hole mimickers, we show how the observed masses and tidal deformabilities of a binary constrain the equation of state. We also show how such constraints can be used to rule out binaries of some simple models of boson stars as possible sources of the simulated events we consider.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.052
GPT teacher head0.451
Teacher spread0.399 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations62
Published2020
Admission routes2
Has abstractyes

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