Characterizing the Host Galaxies and Delay Times of Ca-rich Gap Transients versus 91bg-like SNe and Normal Type Ia SNe
Bibliographic record
Abstract
Abstract Calcium-rich gap transients are a faint, fast-evolving class of supernovae that show strong nebular Ca emission lines. Their progenitor systems are uncertain, but they are often associated with old and quiescent host galaxies. In this work, we compare the properties of the hosts of hydrogen-poor Ca-rich gap transients to the hosts of 3 other classes of supernova (SNe): normal Type Ia, 91bg-like, and Type II. We use data from the Zwicky Transient Facility Census of the Local Universe (CLU) experiment to build up our 4 SNe samples and identify the host galaxies. A combination of precomputed host properties from the CLU catalog and those derived from spectral energy distribution fitting are used to characterize each host’s stellar mass, star formation rate, and specific star formation rate (sSFR). We find that the hosts of Ca-rich gap transients and 91bg-like SNe occupy a similar parameter space of mass and sSFR, and are more massive and quiescent compared to the hosts of Type Ia and Type II SNe. Additionally, we construct delay time distributions (DTDs) for our 4 samples, finding that Ca-rich gap transients and 91bg-like SNe have the longest peak delay times ∼10 4 Myr, compared to the peak delay times of Type Ia SNe (∼10 3 Myr) and Type II SNe (∼10 Myr). The similarity of host environment and DTDs for Ca-rich gap transients and 91bg-like SNe motivates further analysis of the relationship of these two transient classes.
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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 | 0.002 | 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) | 0.001 | 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".