ZTF SN Ia DR2: Secondary maximum in type Ia supernovae
Bibliographic record
Abstract
Type Ia supernova (SN Ia) light curves have a secondary maximum that exists in the r , i , and near-infrared filters. The secondary maximum is relatively weak in the r band, but holds the advantage that it is accessible, even at high redshift. We used Gaussian process fitting to parameterise the light curves of 893 SNe Ia from the Zwicky Transient Facility’s (ZTF) second data release (DR2), and we were able to extract information about the timing and strength of the secondary maximum. We found > 5 σ correlations between the light curve dec rate (Δ m 15 ( g )) and the timing and strength of the secondary maximum in the r band. Whilst the timing of the secondary maximum in the i band is also correlated with Δ m 15 ( g ), the strength of the secondary maximum in the i band shows significant scatter as a function of Δ m 15 ( g ). We found that the transparency timescales of 97 per cent of our sample are consistent with double detonation models and that SNe Ia with small transparency timescales (< 32 d) reside predominantly in locally red environments. We measured the total ejected mass for the normal SNe Ia in our sample using two methods and both were consistent with medians of 1.3 ± 0.3 and 1.2 ± 0.2 M ⊙ . We find that the strength of the secondary maximum is a better standardisation parameter than the SALT light curve stretch ( x 1 ). Finally, we identified a spectral feature in the r band as Fe II , which strengthens during the onset of the secondary maximum. The same feature begins to strengthen at < 3 d post maximum light in 91bg-like SNe. Finally, the correlation between x 1 and the strength of the secondary maximum was best fit with a broken, with a split at x 1 0 = − 0.5 ± 0.2, suggestive of the existence of two populations of SNe Ia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".