Testing Gravitational Memory Generation with Compact Binary Mergers
Bibliographic record
Abstract
Gravitational memory is an important prediction of General Relativity, which is intimately related to asymptotic symmetries at null infinity and the so-called soft graviton theorem. For a given transient astronomical event, the angular distribution of energy and angular momentum fluxes uniquely determine the displacement and spin memory effect in the sky. We investigate the possibility of using the binary black hole merger events detected by Advanced LIGO/Virgo to test the relation between the source's energy emission and the gravitational memory measured on Earth, as predicted by General Relativity. We find that while it is difficult for Advanced LIGO/Virgo one-year detection of a third-generation detector network will easily rule out the hypothesis assuming isotropic memory distribution. In addition, we construct a phenomenological model for memory waveforms of binary neutron star mergers and use it to address the detectability of memory from these events in the third-generation detector era. We find that measuring gravitational memory from neutron star mergers is a possible way to distinguish between different neutron star equations of state.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".