ZTF SN Ia DR2: Properties of the low-mass host galaxies of Type Ia supernovae in a volume-limited sample
Bibliographic record
Abstract
In this study, we explore the characteristics of ‘low-mass’ (log( M ⋆ / M ⊙ ) ≤ 8) and ‘intermediate-mass’ (8 < log( M ⋆ / M ⊙ ) ≤ 10) host galaxies of Type Ia supernovae (SNe Ia) from the second data release (DR2) of the Zwicky Transient Facility survey. We investigated their correlations with different sub-types of SNe Ia. We used the photospheric velocities measured from the Si II λ 6355 feature, SALT2 light-curve stretch ( x 1 ), and host-galaxy properties of SNe Ia to re-investigate the existing relationship between host galaxy mass and Si II λ 6355 velocities. We also investigated sub-type preferences for host populations. We show that the more energetic and brighter 91T-like SNe Ia tend to reside among the younger host populations, while 91bg-like SNe Ia are found among the older populations. Our findings suggest that high-velocity SNe Ia (HV SNe Ia) do indeed come from older populations, but they can also come from young populations as well. Therefore, while our findings can partly provide support for HV SNe Ia in the context of single degenerate progenitor models, they indicate that HV SNe Ia (rather than comprising a different population) might be a continued distribution with different explosion mechanisms. Lastly, we investigate the specific rate of SNe Ia in the volume-limited SN Ia sample of DR2 and compare our results with other 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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".