The Radio Luminosity-risetime Function of Core-collapse Supernovae
Bibliographic record
Abstract
Abstract We assemble a large set of 2–10 GHz radio flux density measurements and upper limits of 294 different supernovae (SNe), from the literature and our own and archival data. Only 31% of SNe were detected. We characterize the SN radio lightcurves near the peak using a two-parameter model, with t pk being the time to rise to a peak and L pk the spectral luminosity at that peak. Over all SNe in our sample at D < 100 Mpc, we find that t pk = 101.7±0.9 days and that L pk = 1025.5±1.6 erg s−1 Hz−1, and therefore that generally 50% of SNe will have L pk < 1025.5 erg s−1 Hz−1. These L pk values are ∼30 times lower than those for only detected SNe. Types Ib/c and II (excluding IIn’s) have similar mean values of L pk but the former have a wider range, whereas Type IIn SNe have ∼10 times higher values with L pk = 1026.5±1.1 erg s−1 Hz−1. As for t pk, Type Ib/c have t pk of only 101.1±0.5 days while Type II have t pk = 101.6±1.0 and Type IIn the longest timescales with t pk = 103.1±0.7 days. We also estimate the distribution of progenitor mass-loss rates, , and find that the mean and standard deviation of are −5.4 ± 1.2 (assuming v wind = 1000 km s−1) for Type Ib/c SNe, and −6.9 ± 1.4 (assuming v wind = 10 km s−1) for Type II SNe excluding Type IIn.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.001 |
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".