Spinning Spectral Sirens: Robust Cosmological Measurement Using Mass–Spin Correlations in the Binary Black Hole Population
Bibliographic record
Abstract
Abstract Gravitational waves from compact binary mergers provide a direct measurement of luminosity distance, which, in combination with redshift information, serves as a cosmological probe. In order to statistically infer merger redshifts, the “spectral standard siren” method relies on features, such as peaks, dips, or breaks, in the compact object mass spectrum, which get redshifted in the detector frame relative to the source frame. However, if the source-frame location of these features evolves over cosmic time, the spectral siren measurement may be biased. Some features, such as the edges of the pair-instability supernova mass gap, may be more stable than others. We point out that binary black hole (BBH) spins, which are not redshifted in the detector frame, provide a natural way to identify robust mass scales for spectral siren cosmology. For example, there is recent evidence for a mass scale in the BBH population that separates slowly spinning from more rapidly spinning BBH mergers, consistent with the lower edge of the pair-instability gap. Applying our method to data from LIGO-Virgo-KAGRA’s third transient catalog, we demonstrate how to isolate this mass scale and produce a robust “spinning spectral siren” measurement of the Hubble constant H 0 = 8 5 − 67 + 99 km s − 1 Mpc − 1 , or H 0 = 8 0 − 46 + 60 km s − 1 Mpc − 1 when combined with other mass features, such as the ∼35 M ⊙ peak. We consider the possibility that the source-frame location of the ∼35 M ⊙ peak evolves with redshift and show that information from black hole spin can be used to mitigate the associated bias for self-calibrating spectral sirens.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".