The Nickel Mass Distribution of Stripped-Envelope Supernovae: Implications for Additional Power Sources
Bibliographic record
Abstract
We perform a systematic study of the $^{56}$Ni mass ($M_{\rm Ni}$) of 27 stripped envelope supernovae (SESNe) by modeling their light-curve tails, highlighting that use of ``Arnett's rule'' overestimates $M_{\rm Ni}$ for SESN by a factor of $\sim$2. Recently, \citet{Khatami2019} presented a new model relating the peak time ($t_{\rm p}$) and luminosity ($L_{\rm p}$) of a radioactive-powered SN to its $M_{\rm Ni}$ that addresses several limitations of Arnett-like models, but depends on a dimensionless parameter, $\beta$. Using observed $t_{\rm p}$, $L_{\rm p}$, and tail-measured $M_{\rm Ni}$ values for 27 SESN, we observationally calibrate $\beta$ for the first time. Despite scatter, we demonstrate that the model of \citet{Khatami2019} with empirically-calibrated $\beta$ values provides significantly improved measurements of $M_{\rm Ni}$ when only photospheric data is available. However, these observationally-constrained $\beta$ values are systematically lower than those inferred from numerical simulations, primarily because the observed sample has significantly higher (0.2-0.4 dex) $L_{\rm p}$ for a given $M_{\rm Ni}$. While effects due to composition, mixing, and asymmetry can increase $L_{\rm p}$ current models cannot explain the systematically low $\beta$ values. However, the discrepancy can be alleviated if $\sim$7--50\% of $L_{\rm p}$ for the observed sample originates from sources other than $^{56}$Ni. Either shock cooling or magnetar spin-down could provide the requisite luminosity. Finally, we find that even with our improved measurements, the $M_{\rm Ni}$ values of SESN are still a factor of $\sim$3 larger than those of hydrogen-rich Type II SN, indicating that these supernovae are inherently different in terms of their progenitor initial mass distributions or 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".