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Record W4412413660 · doi:10.1088/1361-6382/adef8d

Measuring black hole spin through gravitational lensing of pulsars

2025· article· en· W4412413660 on OpenAlexaff
Amjad Ashoorioon, Mohammad Bagher Jahani Poshteh, Robert B. Mann

Bibliographic record

VenueClassical and Quantum Gravity · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsPerimeter InstituteUniversity of Waterloo
Fundersnot available
KeywordsPhysicsPulsarStrong gravitational lensingBlack hole (networking)Gravitational lensAstrophysicsSpin (aerodynamics)Gravitational lensing formalismGravitationGravitational waveAstronomyTheoretical physicsGalaxy

Abstract

fetched live from OpenAlex

Abstract We propose a new procedure for measuring the spin of a black hole with an unprecedented accuracy based on the gravitational lensing of millisecond pulsars. We derive the basic equations for lensing by a rotating black hole. We show that the frame dragging effect increases the deflection angle of a light ray co-rotating with the black hole. For the primary (secondary) images the angular positions are larger (smaller) for a rotating black hole by an amount on the order of tens of microarcseconds. The differential time delay of images for the case in which the lens is a rotating black hole is smaller than that in the case of non-rotating lens of the same mass, and it can be larger than a few milliseconds. We show that this quantity offers the possibility of reducing the error of spin measurement to less than one percent if we could measure the differential time delay with accuracy of microseconds. We also study relativistic images that are produced by light rays that rotate around the black hole before reaching the observer. The angular positions of relativistic images on the same side as the primary (secondary) image are a few microarcseconds larger (smaller) if the black hole is rotating. Furthermore, the differential time delay between relativistic images is about twelve orders of magnitude larger in the case of rotating lens.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

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

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 head0.037
GPT teacher head0.341
Teacher spread0.304 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

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