CORE-COLLAPSE SUPERNOVAE FROM THE PALOMAR TRANSIENT FACTORY: INDICATIONS FOR A DIFFERENT POPULATION IN DWARF GALAXIES
Bibliographic record
Abstract
We use the first compilation of 72 core-collapse supernovae (SNe) from the Palomar Transient Factory (PTF) to study their observed subtype distribution in dwarf galaxies compared to giant galaxies. Our sample is the largest single-survey, untargeted, spectroscopically classified, homogeneous collection of core-collapse events ever assembled, spanning a wide host-galaxy luminosity range (down to M r ≈ −14 mag) and including a substantial fraction (>20%) of dwarf ( M r ⩾ −18 mag) hosts. We find more core-collapse SNe in dwarf galaxies than expected and several interesting trends emerge. We use detailed subclassifications of stripped-envelope core-collapse SNe and find that all Type I core-collapse events occurring in dwarf galaxies are either SNe Ib or broad-lined SNe Ic (SNe Ic-BL), while "normal" SNe Ic dominate in giant galaxies. We also see a significant excess of SNe IIb in dwarf hosts. We hypothesize that in lower metallicity hosts, metallicity-driven mass loss is reduced, allowing massive stars that would have appeared as "normal" SNe Ic in metal-rich galaxies to retain some He and H, exploding as Ib/IIb events. At the same time, another mechanism allows some stars to undergo extensive stripping and explode as SNe Ic-BL (and presumably also as long-duration gamma-ray bursts). Our results are still limited by small-number statistics, and our measurements of the observed N (Ib/c)/ N (II) number ratio in dwarf and giant hosts (0.25 +0.3 −0.15 and 0.23 +0.11 −0.08 , respectively; 1σ uncertainties) are consistent with previous studies and theoretical predictions. As additional PTF data accumulate, more robust statistical analyses will be possible, allowing the evolution of massive stars to be probed via the dwarf-galaxy SN population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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".