Weak-lensing magnification of Type Ia supernovae from the Pantheon sample
Bibliographic record
Abstract
ABSTRACT Using data from the Pantheon Type Ia supernovae (SN Ia) compilation and the Sloan Digital Sky Survey, we propose an estimator for weak-lensing convergence incorporating positional and photometric data of foreground galaxies. The correlation between this and the Hubble diagram residuals of the supernovae has 3.6σ significance, and is consistent with weak-lensing magnification due to dark matter haloes centred on galaxies. We additionally constrain the properties of the galactic haloes, such as the mass-to-light ratio Γ and radial profile of the halo matter density ρ(r). We derive a new relationship for the additional rms scatter in magnitudes caused by lensing, finding σlens = (0.06 ± 0.017)(dC(z)/dC(z = 1))3/2, where dC(z) is the comoving distance to redshift z. Hence, the scatter in apparent magnitudes due lensing will be of the same size as the intrinsic scatter of SN Ia by z ∼ 1.2. We propose a modification of the distance modulus estimator for SN Ia to incorporate lensing, which can be easily calculated from observational data. We anticipate this will improve the accuracy of cosmological parameter estimation for high-redshift SN Ia data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".