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Record W4381712615 · doi:10.48550/arxiv.2306.11787

Gravitational Radiation from hyperbolic encounters in the presence of dark matter

2023· preprint· en· W4381712615 on OpenAlexfundno aff
Abhishek Chowdhuri, Rishabh Kumar Singh, Kaushik Kangsabanik, Arpan Bhattacharyya

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsnot available
FundersUniversidad de MurciaScience and Engineering Research BoardIndian Institute of Technology GandhinagarUniversity of Lethbridge
KeywordsPhysicsGravitational waveDark matterPerturbation (astronomy)Binary numberGravitationRadiationDynamical frictionAstrophysicsClassical mechanicsAstronomyOpticsMathematics

Abstract

fetched live from OpenAlex

In this study, we look into binaries undergoing gravitational radiation during a hyperbolic passage. Such hyperbolic events can be a credible source of gravitational waves in future detectors. We systematically calculate fluxes of gravitational radiation from such events in the presence of dark matter with different profiles, also considering the effects of dynamical friction. We provide an estimate for the braking index and show how it evolves due to the presence of the dark matter medium. We also investigate the binary dynamics through the changes in the orbital parameters by treating the potential due to dark matter spike and the dynamical friction effects as a perturbation term. An insight into the effects of such a medium on the binaries from the corresponding osculating elements opens up avenues to study binary dynamics for such events.

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 score0.001
Threshold uncertainty score0.004

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 studies0.0000.000
Scholarly communication0.0010.001
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 head0.050
GPT teacher head0.247
Teacher spread0.197 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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