CORRELATIONS BETWEEN SDSS TYPE Ia SUPERNOVA RATES AND HOST GALAXY PROPERTIES
Bibliographic record
Abstract
Studying the correlation of Type Ia supernova rates (SNRs) with host galaxy properties is an important step in understanding the exact nature of Type Ia supernovae (SNe Ia). We use SNe Ia from the SDSS-II sample, spectroscopically determined masses and star formation rates, and a new maximum likelihood method, to fit the Scannapieco and Bildsten rate model SNR = A × M + B × SFR, where M is galaxy mass and SFR is star formation rate. We find A = 3.5 +0.9 −0.7 × 10 −14 (SNe/yr)( M ☉ ) −1 and B = 1.3 +0.4 −0.3 × 10 −3 (SNe/yr)( M ☉ yr −1 ) −1 , assuming overall efficiency of 0.5. This is in reasonable agreement with other determinations. However we find strong evidence that this model is a poor fit to other projections of the data: it fails to correctly predict the distribution of supernovae with host mass or SFR. An additional model parameter is required; most likely this parameter is related to host galaxy mass. Some implications of this result are discussed.
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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 | 0.008 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".