Potential biases and prospects for the Hubble constant estimation via electromagnetic and gravitational-wave joint analyses
Bibliographic record
Abstract
ABSTRACT GW170817 is a binary neutron star merger that exhibited a gravitational wave (GW) and a gamma-ray burst, followed by an afterglow. In this work, we estimate the Hubble constant (H0) using broad-band afterglow emission and relativistic jet motion from the Very Long Baseline Interferometry and HST images of GW170817. Compared to previous attempts, we combine these messengers with GW in a simultaneous Bayesian fit. We probe the H0 measurement robustness depending on the data set used, the assumed jet model, the possible presence of a late time flux excess. Using the sole GW leads to a 20 per cent error ($77^{+21}_{-10}$ $\rm km\, s^{-1}\, Mpc^{-1}$, medians, 16th–84th percentiles), because of the degeneracy between viewing angle (θv) and luminosity distance (dL). The latter is reduced by the inclusion in the fit of the afterglow light curve, leading to $H_0=96^{+13}_{-10}$ $\rm km\, s^{-1}\, Mpc^{-1}$, a large value, caused by the fit preference for high viewing angles due to the possible presence of a late-time excess in the afterglow flux. Accounting for the latter by including a constant flux component at late times brings $H_0=78.5^{+7.9}_{-6.4}$$\rm km\, s^{-1}\, Mpc^{-1}$. Adding the centroid motion in the analysis efficiently breaks, the dL − θv degeneracy and overcome the late-time deviations, giving $H_0 = 69.0^{+4.4}_{-4.3}$ $\rm km\, s^{-1}\, Mpc^{-1}$ (in agreement with Planck and SH0ES measurements) and $\theta _{\rm v} = 18.2^{+1.2}_{-1.5}$°. This is valid regardless of the jet structure assumption. Our simulations show that for next GW runs radio observations are expected to provide at most few other similar events.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".