Clustering of Supernova Ia Host Galaxies
Bibliographic record
Abstract
For the first time the cross-correlation between Type Ia supernova host galaxies and surrounding field galaxies is measured using the Supernova Legacy Survey sample. Over the z = 0.2–0.9 redshift range we find that supernova hosts are correlated an average of 60% more strongly than similarly selected field galaxies over the 3''-100'' range and about a factor of 3 more strongly below 10''. The correlation errors are empirically established with a jackknife analysis of the four SNLS fields. The hosts are more correlated than the field at a significance of 99% in the fitted amplitude and slope, with the point-by-point difference of the two correlation functions having a reduced χ 2 for 8 degrees of freedom of 4.3, which has a probability of random occurrence of less than 3 × 10 −5 . The correlation angle is 1.5'' ± 0.5'', which deprojects to a fixed comoving correlation length of approximately 6.5 ± 2 h −1 Mpc . Weighting the field galaxies with the mass and star formation rate supernova frequencies of the simple A + B model produces good agreement with the observed clustering. We conclude that these supernova clustering differences are primarily the expected outcome of the dependence of supernova rates on galaxy masses and stellar populations with their clustering environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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) | 0.008 | 0.001 |
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".