The Type Ibn Supernova 2019kbj: Indications for Diversity in Type Ibn Supernova Progenitors
Bibliographic record
Abstract
Abstract Type Ibn supernovae (SNe) are a rare class of stellar explosions whose progenitor systems are not yet well determined. We present and analyze observations of the Type Ibn SN 2019kbj, and model its light curve in order to constrain its progenitor and explosion parameters. SN 2019kbj shows roughly constant temperature during the first month after peak, indicating a power source (likely circumstellar material interaction) that keeps the continuum emission hot at ∼15,000 K. Indeed, we find that the radioactive decay of 56 Ni is disfavored as the sole power source of the bolometric light curve. A radioactive decay + circumstellar material (CSM) interaction model, on the other hand, does reproduce the bolometric emission well. The fits prefer a uniform-density CSM shell rather than CSM due to a steady mass-loss wind, similar to what is seen in other Type Ibn SNe. The uniform-density CSM shell model requires ∼0.1 M ⊙ of 56 Ni and ∼1 M ⊙ total ejecta mass to reproduce the light curve. SN 2019kbj differs in this manner from another Type Ibn SN with derived physical parameters, SN 2019uo, for which an order of magnitude lower 56 Ni mass and larger ejecta mass were derived. This points toward a possible diversity in SN Ibn progenitor systems and explosions.
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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 | 0.001 | 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 | 0.000 |
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".