Inferring Host-galaxy Properties of LIGO–Virgo–KAGRA’s Black Holes
Bibliographic record
Abstract
Abstract Observations of gravitational waves from binary black hole (BBH) mergers have measured the redshift evolution of the BBH merger rate. The number density of galaxies in the Universe evolves differently with redshift based on their physical properties, such as their stellar masses and star formation rates. In this work we show that the measured population-level redshift distribution of BBHs sheds light on the properties of their probable host galaxies. We first assume that the hosts of BBHs can be described by a mixture model of galaxies weighted by stellar mass or star formation rate, and find that we can place upper limits on the fraction of mergers coming from a stellar-mass-weighted sample of galaxies. We then constrain the parameters of a physically motivated power-law delay-time distribution using GWTC-3 data, and self-consistently track galaxies in the UniverseMachine simulations with this delay-time model to infer the probable host galaxies of BBHs over a range of redshifts. We find that the inferred host galaxy distribution at redshift z = 0.21 has a median star formation rate ∼ 0.9 M ⊙ yr−1 and a median stellar mass of ∼1.9 × 1010 M ⊙. We also provide distributions for the mean stellar age, halo mass, halo radius, peculiar velocity, and large-scale bias associated with the host galaxies, as well as their absolute magnitudes in the B and Ks bands. Our results can be used to design optimal electromagnetic follow-up strategies for BBHs, and also to aid the measurement of cosmological parameters using the statistical dark-siren method.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".