Joint analysis of gravitational-wave and electromagnetic data of mergers: breaking an afterglow model degeneracy in GW170817 and in future events
Bibliographic record
Abstract
ABSTRACT On 2017 August 17, Advanced LIGO and Virgo observed GW170817, the first gravitational-wave (GW) signal from a binary neutron star merger. It was followed by a short-duration gamma-ray burst, GRB 170817A, and by a non-thermal afterglow emission. In this work, a combined simultaneous fit of the electromagnetic (EM, specifically, afterglow) and GW domains is implemented, both using the posterior distribution of a GW standalone analysis as prior distribution to separately process the EM data, and fitting the EM and GW domains simultaneously. These approaches coincide mathematically, as long as the actual posterior of the GW analysis, and not an approximation, is used as prior for the EM analysis. We treat the viewing angle, θv, as shared parameter across the two domains. In the afterglow modelling with a Gaussian structured jet this parameter and the jet core angle, θc, are correlated, leading to high uncertainties on their values. The joint EM + GW analysis relaxes this degeneracy, reducing the uncertainty compared to an EM-only fit. We also apply our methodology to hypothetical GW170817-like events occurring in the next GW observing run at ∼140 and 70 Mpc. At 70 Mpc the existing EM degeneracy is broken, thanks to the inclusion of the GW domain in the analysis. At 140 Mpc, the EM-only fit cannot constrain θv nor θc because of the lack of detections in the afterglow rising phase. Folding the GW data into the analysis leads to tighter constraints on θv, still leaving θc unconstrained, requiring instruments with higher sensitivities, such as Athena.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".