Infant Type Ia Supernovae from the KMTNet. I. Multicolor Evolution and Populations
Bibliographic record
Abstract
Abstract We conduct a systematic analysis of the early multiband light curves and colors of 19 Type Ia supernovae (SNe) from the Korea Microlensing Telescope Network SN Program, including 16 previously unpublished events. Seven are detected ≲1 day (as early as ≲1 hr) since the estimated epoch of first light and the rest ≲3 days. Some show excess emission within <0.5 day to ∼2 days, but most show pure power-law rises. Colors are initially diverse before ∼5 days, but converge to similar values at ∼10 days. We identify at least three populations based on 2–5 day color evolution: (1) “early-blues” exhibit slowly evolving colors consistent with a ∼17,000 K blackbody; (2) “early-reds” have initially blue B − V and red V − i colors that cannot simultaneously be fit with a blackbody—likely due to suppression of B- and i-band flux by Fe ii/iii and Ca ii—and evolve more rapidly; and (3) “early-yellows” evolve blueward, consistent with thermal heating from ∼8000–13,000 K. Distributions of early-blue and early-red colors are compatible with them being either distinct populations—with early-reds comprising (60 ± 15)% of them—or extreme ends of one continuous population, whereas the early-yellow population identified here is clearly distinct. Compared to the other populations, early-blues in our sample differ by exhibiting excess emission within 1–2 days, nearly constant peak brightness regardless of Δm 15(B) after standardization, and shallower Si ii features. Early-blues also prefer star-forming host environments, while early-yellows and, to a lesser extent, early-reds prefer quiescent ones. These preferences appear to indicate at least two Type Ia SN production channels based on stellar population age, while early-reds and early-blues may still share a common origin.
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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 | 0.001 |
| 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".