Probing the large scale structure using gravitational-wave observations of binary black holes
Bibliographic record
Abstract
Third generation gravitational-wave (GW) detectors are expected to detect a large number of binary black holes (BBHs) to large redshifts, opening up an independent probe of the large scale structure using their clustering. This probe will be complementary to the probes using galaxy clustering---GW events could be observed up to very large redshifts ($z\ensuremath{\sim}10$) although the source localization will be much poorer at large distances ($\ensuremath{\sim}\mathrm{tens}$ of square degrees). We explore the possibility of probing the large scale structure from the spatial distribution of the observed BBH population, using their two-point (auto)correlation function. We find that we can estimate the bias factor of the population of BBHs (up to $z\ensuremath{\sim}0.7$) with a few years of observations with these detectors. Our method relies solely on the source-location posteriors obtained from the GW events and does not require any information from electromagnetic observations. This will help in identifying the types of galaxies that host the BBH population, thus shedding light on their origins.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".