Systematically Revisiting All NuSTAR Spins of Black Holes in X-Ray Binaries
Bibliographic record
Abstract
Abstract We extend our recent work on black hole spin in X-ray binary systems to include an analysis of 189 archival NuSTAR observations from 24 sources. Using self-consistent data reduction pipelines, spectral models, and statistical techniques, we report an unprecedented and uniform sample of 36 stellar-mass black hole spin measurements based on relativistic reflection. This treatment suggests that prior reports of low spins in a small number of sources were generally erroneous: our comprehensive treatment finds that those sources tend to harbor black holes with high spin values. Overall, within 1 σ uncertainty, ∼86% of the sample are consistent with a ≥ 0.95, ∼94% of the sample are consistent with a ≥ 0.9, and 100% are consistent with a ≥ 0.7 (the theoretical maximum for neutron stars; a = cJ / GM 2 ). We also find that the high-mass X-ray binaries (those with A-, B-, or O-type companions) are consistent with a ≥ 0.9 within the 1 σ errors; this is in agreement with the low-mass X-ray binary population and may be especially important for comparisons to black holes discovered in gravitational wave events. In some cases, different spectra from the same source yield similar spin measurements but conflicting values for the inclination of the inner disk; we suggest that this is due to variable disk winds obscuring the blue wing of the relativistic Fe K emission line. We discuss the implications of our measurements, the unique view of systematic uncertainties enabled by our treatment, and future efforts to characterize black hole spins with new missions.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".