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Record W4385813532 · doi:10.3847/1538-4357/ace1eb

On the Tidal Capture of White Dwarfs by Intermediate-mass Black Holes in Dense Stellar Environments

2023· article· en· W4385813532 on OpenAlexafffund
Claire S. Ye, Giacomo Fragione, Rosalba Perna

Bibliographic record

VenueThe Astrophysical Journal · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsPhysicsAstrophysicsWhite dwarfGlobular clusterSupermassive black holeGravitational waveIntermediate-mass black holeStar clusterEinstein TelescopeBlack hole (networking)Star (game theory)StarsAstronomyGalaxy

Abstract

fetched live from OpenAlex

Abstract Intermediate-mass black holes (IMBHs) are the missing link between stellar-mass and supermassive black holes, widely believed to reside in at least some dense star clusters, but not yet observed directly. Tidal disruptions of white dwarfs (WDs) are luminous only for black holes less massive than ∼105 M ⊙, therefore providing a unique smoking gun that could finally prove the existence of IMBHs beyond any reasonable doubt. Here, we investigate the tidal captures of WDs by IMBHs in dense star clusters, and estimate upper limits to the capture rates of ∼1 Myr−1 for galactic nuclei and ∼0.01 Myr−1 for globular clusters. Following the capture, the WD inspirals onto the IMBH, producing gravitational waves detectable out to ∼100 Mpc by LISA for ∼104 M ⊙ IMBHs. The subsequent tidal stripping/disruption of the WD can also release bright X-ray and gamma-ray emission with luminosities of at least ≳1040 erg s−1, detectable by Chandra, Swift, and upcoming telescopes, such as the Einstein Probe.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.269
Teacher spread0.260 · 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 designSimulation or modeling
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

Citations11
Published2023
Admission routes2
Has abstractyes

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