Prospects of discovering subsolar primordial black holes using the stochastic gravitational wave background from third-generation detectors
Bibliographic record
Abstract
ABSTRACT Primordial black holes (PBHs) are dark matter candidates that span broad mass ranges from 10−17 M⊙ to ∼100 M⊙. We show that the stochastic gravitational wave background can be a powerful window for the detection of subsolar mass PBHs and shed light on their formation channel via third-generation gravitational wave detectors such as Cosmic Explorer and the Einstein Telescope. By using the mass distribution of the compact objects and the redshift evolution of the merger rates, we can distinguish astrophysical sources from PBHs and will be able to constrain the fraction of subsolar mass PBHs ≤1 M⊙ in the form of dark matter $f_\mathrm{PBH}\le 1{{\ \rm per\ cent}}$ at $68{{\ \rm per\ cent}}$ C.L. even for a pessimistic value of a binary suppression factor. In the absence of any suppression of the merger rate, constraints on fPBH will be less than $0.001{{\ \rm per\ cent}}$. Furthermore, we will be able to measure the redshift evolution of the PBH merger rate with about $1{{\ \rm per\ cent}}$ accuracy, making it possible to uniquely distinguish between the Poisson and clustered PBH scenarios.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".