Probing primordial stochastic gravitational wave background with multi-band astrophysical foreground cleaning
Bibliographic record
Abstract
Abstract The primordial stochastic gravitational wave background (SGWB) carries first-hand messages of early-universe physics, possibly including effects from inflation, preheating, cosmic strings, electroweak symmetry breaking, and etc. However, the astrophysical foreground from compact binaries may mask the SGWB, introducing difficulties in detecting the signal and measuring it accurately. In this paper, we propose a foreground cleaning method taking advantage of gravitational wave observations in other frequency bands. We apply this method to probing the SGWB with space-borne gravitational wave detectors, such as the laser interferometer space antenna (LISA). We find that the spectral density of the LISA-band astrophysical foreground from compact binaries (black holes and neutron stars) can be predicted with percent-level accuracy assuming 10-years’ observations of third-generation GW detectors, e.g., cosmic explorer. While this multi-band method does not apply to binary white dwarfs (BWDs) which usually merger before entering the frequency band of ground-based detectors, we limit our foreground cleaning to frequency higher than ∼5 mHz, where all galactic BWDs can be individually resolved by LISA and the shape of the spectral density of the foreground from extragalactic BWDs can be reconstructed and/or modeled with certain uncertainties. After the foreground cleaning, LISA’s sensitivity to the primordial SGWB will be substantially improved for either two LISA constellations where SGWB can be measured by cross correlating their outputs or only one constellation with three spacecrafts where SGWB can be measured by contrasting the responses of a signal channel and a null channel.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".