Improving the detection sensitivity to primordial stochastic gravitational waves with reduced astrophysical foregrounds
Bibliographic record
Abstract
One of the primary targets of third-generation (3G) ground-based gravitational wave (GW) detectors is detecting the stochastic GW background (SGWB) from early Universe processes. The astrophysical foreground from compact binary mergers will be a major contamination to the background, which must be reduced to high precision to enable the detection of the primordial background. In this work, we revisit the limit of foreground reduction computed in previous studies, point out potential problems in previous foreground cleaning methods, and propose a novel cleaning method subtracting the approximate signal strain and removing the average residual power. With this method, the binary black hole foreground is reduced with fractional residual energy density below ${10}^{\ensuremath{-}4}$ for frequency $f\ensuremath{\in}(10,{10}^{2})\text{ }\text{ }\mathrm{Hz}$, below ${10}^{\ensuremath{-}3}$ for frequency $f\ensuremath{\in}({10}^{2},{10}^{3})\text{ }\text{ }\mathrm{Hz}$ and below the detector sensitivity limit for all relevant frequencies in our simulations. Similar precision is achieved to clean the foreground from binary neutron stars (BNSs) that are above the detection threshold, so that the residual foreground is dominated by subthreshold BNSs, which will be the next critical problem to solve for detecting the primordial SGWB in the 3G era.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".