Investigating helium-layer stripping in the interacting SN 2021efd
Bibliographic record
Abstract
Some stars meet their ends in supernovae (SN) explosions. Massive stars (>8M_sun) at the end of their stellar evolution suffer a core-collapse and explode as core-collapse supernovae (CCSNe). SNe have diverse observational properties and are divided into classes based on them. Much can be known about the star that exploded from the SN it produced. \nStripped-envelope supernovae (SESNe) are explosions of massive stars that lost a part of the hydrogen envelope (Type IIb), the whole hydrogen envelope (Type Ib), or the hydrogen envelope and the helium layer (Type Ic). Evidence has accumulated to support a binary interaction as the stripping mechanism for the hydrogen envelope. The mechanism responsible for the helium-layer stripping for the progenitors of Type Ic SNe is still an open question. \nIn this thesis, I performed an in-depth analysis of the spectroscopic and photometric data of the peculiar Type Ib SN 2021efd. The light curve of SN 2021efd has an excess at late phases compared to the level that is expected from the decay of Nickel-56. I analyzed the spectra of SN 2021efd and concluded that the excess luminosity was caused by interaction of the ejecta with hydrogen-poor circumstellar material. I derived the ejecta parameters and the progenitor star mass and concluded that they do not separate SN 2021efd from the general population of SESNe. I estimated the mass-loss rate of the progenitor star by comparing the interaction luminosity in SN 2021efd to numerical calculations. Based on the high mass-loss rate, I concluded that the mass-loss mechanism is not consistent with line-driven wind. Instead, I suggest that the mass loss could have happened in eruptions. Based on the high mass-loss rate, I concluded that the mass-loss mechanism is not consistent with line-driven wind. Instead, I suggest that the mass loss could have happened in eruptions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".