Peculiar-velocity cosmology with Types Ia and II supernovae
Bibliographic record
Abstract
ABSTRACT We present the Democratic Samples of Supernovae (DSS), a compilation of 775 low-redshift Type Ia and II supernovae (SNe Ia and II), of which 137 SN Ia distances are derived via the newly developed snapshot distance method. Using the objects in the DSS as tracers of the peculiar-velocity field, we compare against the corresponding reconstruction from the 2M++ galaxy redshift survey. Our analysis – which takes special care to properly weight each DSS subcatalogue and cross-calibrate the relative distance scales between them – results in a measurement of the cosmological parameter combination $f\sigma _8 = 0.390_{-0.022}^{+0.022}$ as well as an external bulk flow velocity of $195_{-23}^{+22}$ km s−1 in the direction $(\ell , b) = (292_{-7}^{+7}, -6_{-4}^{+5})$ deg, which originates from beyond the 2M++ reconstruction. Similarly, we find a bulk flow of $245_{-31}^{+32}$ km s−1 towards $(\ell , b) = (294_{-7}^{+7}, 3_{-5}^{+6})$ deg on a scale of $\sim 30\, h^{-1}$ Mpc if we ignore the reconstructed peculiar-velocity field altogether. Our constraint on fσ8 – the tightest derived from SNe to date (considering only statistical error bars), and the only one to utilize SNe II – is broadly consistent with other results from the literature. We intend for our data accumulation and treatment techniques to become the prototype for future studies that will exploit the unprecedented data volume from upcoming wide-field surveys.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".