The Nickel Mass Distribution of Stripped-envelope Supernovae: Implications for Additional Power Sources
Bibliographic record
Abstract
Abstract We perform a systematic study of the 56Ni mass (M Ni) of 27 stripped-envelope supernovae (SESNe) by modeling their light-curve tails, highlighting that use of “Arnett’s rule” overestimates M Ni for SESNe by a factor of ∼2. Recently, Khatami & Kasen presented a new model relating the peak time (t p) and luminosity (L p) of a radioactively powered supernova to its M Ni that addresses several limitations of Arnett-like models, but depends on a dimensionless parameter, β. Using observed t p, L p, and tail-measured M Ni values for 27 SESNe, we observationally calibrate β for the first time. Despite scatter, we demonstrate that the model of Khatami & Kasen with empirically calibrated β values provides significantly improved measurements of M Ni when only photospheric data are available. However, these observationally constrained β values are systematically lower than those inferred from numerical simulations, primarily because the observed sample has significantly higher (0.2–0.4 dex) L p for a given M Ni. While effects due to composition, mixing, and asymmetry can increase L p none can explain the systematically low β values. However, the discrepancy can be alleviated if ∼7%–50% of L p for the observed sample comes from sources other than radioactive decay. Either shock cooling or magnetar spin-down could provide the requisite luminosity. Finally, we find that even with our improved measurements, the M Ni values of SESNe are still a factor of ∼3 larger than those of hydrogen-rich Type II SNe, indicating that these supernovae are inherently different in terms of the initial mass distributions of their progenitors or their explosion mechanisms.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".