A joint model fitting of GW and EM data of merger: breaking model degeneracies in GW1707817
Bibliographic record
Abstract
On August 17, 2017, 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, opening the way for multi-messenger studies. In this work, a combined simultaneous fit of the electromagnetic (EM, specifically, the afterglow) and GW domains is implemented, both fitting the EM data with a GW-informed prior and fitting the EM and GW domains simultaneously. This fits are mathematically the same, but modelling the GW posterior instead of using the actual distribution in the former can lead to incorrect results. We treat the viewing angle as a common parameter shared across the two domains. In the EM afterglow modelling this parameter and the jet opening angle are correlated, leading to high uncertainties on their values. The joint EM+GW analysis eases this degeneracy, reducing the uncertainty compared to an EM-only fit. We also apply our methodology to a hypothetical GW170817-like event occurring in the next GW observing run at 136.5 Mpc, so that the afterglow flux is about one order of magnitude fainter, leaving only the peak of the light curve visible. The EM-only fit cannot constrain the viewing angle nor the jet opening angle, while folding the GW data into the analysis leads to tighter constraints only on the viewing angle. Moreover, it is impossible to identify the geometry of the jet, which can either be a top hat or a Gaussian structured jet.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| 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".