Unresolved stochastic background from compact binary mergers detectable by next-generation ground-based gravitational-wave observatories
Bibliographic record
Abstract
The next generation of ground-based gravitational-wave detectors will look much deeper into the Universe and have unprecedented sensitivities and low-frequency capabilities. Especially alluring is the possibility of detecting an early-Universe cosmological stochastic background that could provide important insights into the beginnings of our Universe and fundamental physics at extremely high energies. However, even if next-generation detectors are sensitive to cosmological stochastic backgrounds, they will be masked by more dominant astrophysical backgrounds, namely the residual background from the imperfect subtraction of resolvable compact binary coalescences (CBCs) as well as the CBC background from individually unresolvable CBCs. Using our latest knowledge of masses, rates, and delay time distributions, we present a data-driven estimate of the unresolvable CBC background that will be seen by next-generation detectors. Accounting for statistical errors and astrophysical systematics, this estimate quantifies an important piece in the CBC noise budget for next-generation detectors and can help inform detector design and subtraction algorithms. Importantly, we find that the neutron-star black-hole binary contribution to the unresolved CBC background is further comparable to the binary neutron star contribution. We compare our results with predictions for backgrounds from several cosmological sources in the literature, finding that the unresolvable background will likely be a significant impediment for many models. This motivates the need for simultaneous inference methods or other statistical techniques to detect early-Universe cosmological backgrounds.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".