Circumstellar Interaction Powers the Light Curves of Luminous Rapidly Evolving Optical Transients
Bibliographic record
Abstract
Abstract Rapidly evolving transients, or objects that rise and fade in brightness on timescales two to three times shorter than those of typical Type Ia or Type II supernovae (SNe), have uncertain progenitor systems and powering mechanisms. Recent studies have noted similarities between rapidly evolving transients and Type Ibn SNe, which are powered by ejecta interacting with He-rich circumstellar material (CSM). In this work we present multiband photometric and spectroscopic observations from Las Cumbres Observatory and Swift of four fast-evolving Type Ibn SNe. We compare these observations with those of rapidly evolving transients identified in the literature. We discuss several common characteristics between these two samples, including their light curve and color evolution as well as their spectral features. To investigate a common powering mechanism we construct a grid of analytical model light curves with luminosity inputs from CSM interaction as well as 56 Ni radioactive decay. We find that models with ejecta masses of ≈1–3 M ⊙ , CSM masses of ≈0.2–1 M ⊙ , and CSM radii of ≈20–65 au can explain the diversity of peak luminosities, rise times, and decline rates observed in Type Ibn SNe and rapidly evolving transients. This suggests that a common progenitor system—the core collapse of a high-mass star within a dense CSM shell—can reproduce the light curves of even the most luminous and fast-evolving objects, such as AT 2018cow. This work is one of the first to reproduce the light curves of both SNe Ibn and other rapidly evolving transients with a single model.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".