COSMIC VARIANCE IN THE NANOHERTZ GRAVITATIONAL WAVE BACKGROUND
Bibliographic record
Abstract
ABSTRACT We use large N -body simulations and empirical scaling relations between dark matter halos, galaxies, and supermassive black holes (SMBBHs) to estimate the formation rates of SMBBH binaries and the resulting low-frequency stochastic gravitational wave background (GWB). We find this GWB to be relatively insensitive ( ) to cosmological parameters, with only slight variation between wmap5 and Planck cosmologies. We find that uncertainty in the astrophysical scaling relations changes the amplitude of the GWB by a factor of ∼2. Current observational limits are already constraining this predicted range of models. We investigate the Poisson variance in the amplitude of the GWB for randomly generated populations of SMBBHs, finding a scatter of order unity per frequency bin below 10 nHz, and increasing to a factor of ∼10 near 100 nHz. This variance is a result of the rarity of the most massive binaries, which dominate the signal, and acts as a fundamental uncertainty on the amplitude of the underlying power law spectrum. This Poisson uncertainty dominates at nHz, while at lower frequencies the dominant uncertainty is related to our poor understanding of the astrophysical scaling relations, although very low frequencies may be dominated by uncertainties related to the final parsec problem and the processes which drive binaries to the gravitational wave dominated regime. Cosmological effects are negligible at all frequencies.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".