Probing general relativity in galactic scales at z ∼ 0.3
Bibliographic record
Abstract
ABSTRACT General Relativity (GR) has been successfully tested mainly at Solar system scales; however, galaxy-scale tests have become popular in the last few decades. In this work, we investigate the ηPPN parameter, which is commonly defined by the ratio of two scalar potentials that appears in the cosmological linearly perturbed metric. Under the assumption of GR and a vanish anisotropic stress tensor, ηPPN = 1. Using ALMA, HST, and VLT/MUSE data, we combine mass measurements, using gravitational lensing and galactic dynamics, for the SDP.81 lens galaxy (z = 0.299) to constrain ηPPN. By using a flexible and self-consistent mass profile, our fiducial model takes into account the contribution of the stellar mass and a dark matter halo to reconstruct the lensed galaxy and the spatially resolved stellar kinematics. We infer, after accounting for systematic uncertainties related to the mass model, cosmology, and kinematics, $\eta _{\text{PPN}} = 1.13^{+0.03}_{-0.03}\pm 0.20\, (\text{sys})$, which is in accordance with GR predictions. Better spectroscopy data are needed to push the systematics down and bring the uncertainty to the percentage level since our analysis shows that the main source of the systematics is related to kinematics, which heavily depends on the signal-to-noise ratio of the spectra.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 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".