Supernova 2018cuf: A Type IIP Supernova with a Slow Fall from Plateau
Bibliographic record
Abstract
Abstract We present multiband photometry and spectroscopy of SN 2018cuf, a Type IIP (“P” for plateau) supernova (SN) discovered by the Distance Less Than 40 Mpc Survey within 24 hr of explosion. SN 2018cuf appears to be a typical SN IIP, with an absolute V -band magnitude of −16.73 ± 0.32 at maximum and a decline rate of 0.21 ± 0.05 mag/50 days during the plateau phase. The distance of the object was constrained to be 41.8 ± 5.7 Mpc by using the expanding photosphere method. We used spectroscopic and photometric observations from the first year after the explosion to constrain the progenitor of SN 2018cuf using both hydrodynamic light-curve modeling and late-time spectroscopic modeling. The progenitor of SN 2018cuf was most likely a red supergiant of about 14.5 M ⊙ that produced 0.04 ± 0.01 M ⊙ 56 Ni during the explosion. We also found ∼0.07 M ⊙ of circumstellar material (CSM) around the progenitor is needed to fit the early light curves, where the CSM may originate from presupernova outbursts. During the plateau phase, high-velocity features at ∼11,000 km s −1 were detected in both the optical and near-infrared spectra, supporting the possibility that the ejecta were interacting with some CSM. A very shallow slope during the postplateau phase was also observed, and it is likely due to a low degree of nickel mixing or the relatively high nickel mass in the SN.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".