How much hydrogen is in Type Ib and IIb supernova progenitors?
Bibliographic record
Abstract
ABSTRACT Core-collapse supernovae showing little or no hydrogen (denoted by Type IIb and Ib, respectively) are the explosions of massive stars that have lost some or most of their outer envelopes. How they lose their mass is unclear, but it likely involves binary interaction. So far, seven progenitors of such supernovae have been identified in pre-explosion imaging (five for Type IIb events and two for Type Ib events). Here, we evolve detailed binary stellar evolution models in order to better understand the nature of these progenitors. We find that the amount of hydrogen left in the envelope at the time of explosion greatly depends on the post-interaction mass-loss rate. The leftover hydrogen, in turn, strongly affects progenitor properties, such as temperature and photospheric radius, in non-trivial ways. Together with extinction and distance uncertainties in progenitor data, it is difficult to deduce an accurate progenitor hydrogen mass from pre-explosion imaging. We quantify this uncertainty and find that available data are consistent with a proposed Type Ib–IIb hydrogen mass threshold of ${\approx}0.033\, \mathrm{M}_\odot$, implying that even Type Ib progenitors are not pure helium stars. These results alleviate the proposed tension between the Type Ib classification of SN 2019yvr and its candidate progenitor properties. We also estimate the brightness of a surviving 2019yvr progenitor companion, which might be detected in future observations.
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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) | 0.001 | 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".