Bridging the Gap: Categorizing Gravitational-wave Events at the Transition between Neutron Stars and Black Holes
Bibliographic record
Abstract
Abstract We search for features in the mass distribution of detected compact binary coalescences which signify the transition between neutron stars (NSs) and black holes (BHs). We analyze all gravitational-wave (GW) detections by the LIGO Scientific Collaboration, the Virgo Collaboration, and the KAGRA Collaboration (LVK) made through the end of the first half of the third observing run, and find clear evidence for two different populations of compact objects based solely on GW data. We confidently (99.3%) find a steepening relative to a single power law describing NSs and low-mass BHs below 2.4 − 0.5 + 0.5 M ⊙ , which is consistent with many predictions for the maximum NS mass. We find suggestions of the purported lower mass gap between the most massive NSs and the least massive BHs, but are unable to conclusively resolve it with current data. If it exists, we find the lower mass gap’s edges to lie at 2.2 − 0.5 + 0.7 M ⊙ and 6.0 − 1.4 + 2.4 M ⊙ . We reexamine events that have been deemed “exceptional” by the LVK collaborations in the context of these features. We analyze GW190814 self-consistently in the context of the full population of compact binaries, finding support for its secondary to be either a NS or a lower mass gap object, consistent with previous claims. Our models are the first to accommodate this event, which is an outlier with respect to the binary BH population. We find that GW200105 and GW200115 probe the edges of, and may have components within, the lower mass gap. As future data improve global population models, the classification of these events will also improve.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".