Observing the post-merger signal of GW170817-like events with improved gravitational-wave detectors
Bibliographic record
Abstract
The recent detection of a neutron star binary through gravitational waves, GW170817, has offered another source of information about the properties of cold supranuclear matter. Information from the signal emitted before the neutron stars merged has been used to study the equation of state of these bodies, however, any complementary information included in the signal emitted after the merger has been lost in the detector noise. In this paper we investigate the prospects of studying GW170817-like post-merger signals with future gravitational-wave detectors. We first compute the expected properties of the possible GW170817 post-merger signal using information from pre-merger analyses. We then quantify the required improvement in detector sensitivity in order to extract key features of the post-merger signal. We find that if we observe a signal of similar strength to GW170817 when the aLIGO detectors have been improved by $\ensuremath{\sim}2--3$ times over their design sensitivity in the kHz regime, we will be able to extract the dominant frequency component of the post-merger. With further improvements and next-generation detectors we will also be able to extract subdominant frequencies. We conclude that post-merger signals could be brought within our reach in the coming years given planned detector upgrades, such as $\mathrm{A}+$, Voyager, and the next-generation detectors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".