STELLAR-MASS BLACK HOLE SPIN CONSTRAINTS FROM DISK REFLECTION AND CONTINUUM MODELING
Bibliographic record
Abstract
Accretion disk reflection spectra, including broad iron emission lines, bear the imprints of the strong Doppler shifts and gravitational redshifts close to black holes. The extremity of these shifts depends on the proximity of the innermost stable circular orbit to the black hole, and that orbit is determined by the black hole spin parameter. Modeling relativistic spectral features, then, gives a means of estimating black hole spin. We report on the results of fits made to archival X-ray spectra of stellar-mass black holes and black hole candidates, selected for strong disk reflection features. Following recent work, these spectra were fit with reflection models and disk continuum emission models (where required) in which black hole spin is a free parameter. Although our results must be regarded as preliminary, we find evidence for a broad range of black hole spin parameters in our sample. The black holes with the most relativistic radio jets are found to have high spin parameters, though jets are observed in a black hole with a low spin parameter. For those sources with constrained binary system parameters, we examine the distribution of spin parameters versus black hole mass, binary mass ratio, and orbital period. We discuss the results within the context of black hole creation events, relativistic jet production, and efforts to probe the innermost relativistic regime around black holes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".