Constraining dark matter halo properties using lensed Supernova Legacy Survey supernovae
Bibliographic record
Abstract
This paper exploits the gravitational magnification of Type Ia supernovae (SNe Ia) to measure properties of dark matter haloes. Gravitationally magnified and de-magnified SNe Ia should be brighter and fainter than average, respectively. The magnification of individual SNe Ia can be computed using observed properties of foreground galaxies and dark matter halo models. We model the dark matter haloes of the galaxies as truncated singular isothermal spheres with velocity dispersion and truncation radius obeying luminosity-dependent scaling laws. A homogeneously selected sample of 175 SNe Ia from the first 3 yr of the Supernova Legacy Survey (SNLS) in the redshift range 0.2 ≲z≲ 1 is used to constrain models of the dark matter haloes associated with foreground galaxies. The best-fitting velocity dispersion scaling law agrees well with galaxy–galaxy lensing measurements. We further find that the normalization of the velocity dispersion of passive and star-forming galaxies are consistent with empirical Faber–Jackson and Tully–Fisher relations, respectively. If we make no assumption on the normalization of these relations, we find that the data prefer gravitational lensing at the 92 per cent confidence level. Using recent models of dust extinction, we deduce that the impact of this effect on our results is very small. We also investigate the brightness scatter of SNe Ia due to gravitational lensing, which has implications for SN Ia cosmology. The gravitational lensing scatter is approximately proportional to the SN Ia redshift. We find the constant of proportionality to be B≃ 0.055+0.039−0.041 mag (B≲ 0.12 mag at the 95 per cent confidence level). If this model is correct, the contribution from lensing to the intrinsic brightness scatter of SNe Ia is small for the SNLS sample. According to the best-fitting dark matter model, gravitational lensing should, however, contribute significantly to the brightness scatter at z≳ 1.6.
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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) | 0.001 | 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".