A new experiment for gravitational wave detection
Bibliographic record
Abstract
A new experiment for gravitational waves (GWs) detection is proposed. It is shown that the effect of GWs on sound waves (SWs) in a fluid is that GWs vary the pressure of the fluid as they pass through it. This variation can be found by analysing the gauge of the local observer. It is shown that one can, in principle, detect GWs through the proposed new experiment. The variation of the pressure of the fluid, which represents detected signals, is indeed much higher than the corresponding values of GW amplitudes. The examples of rotating neutron stars (NSs) and relic GWs are discussed. Remarkably, a comparison of the proposed new method with a previous paper of Singh et al. (New J. Phys. 19, 073023 (2017). doi: 10.1088/1367-2630/aa78cb ) on a similar approach shows a possible improvement of the sensitivity concerning the potential detection of GWs. It must be emphasized that this proposed procedure may be difficult in practical experiments because of the presence of different types of noise. For this reason, a section of the paper is dedicated to the discussion of such noise. On the other hand, this paper must be considered as pioneering the new proposed approach. Thus, we hope that in future more precise studies of the noise that concerns the proposed new experiment will be done.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".