Bibliographic record
Abstract
Context. This is the first paper in a series aiming to determine the fractions and birth rates of various types of supernovae (SNe) in the local Universe. Aims. In this paper, we aim to construct a complete sample of SNe in the nearby Universe and provide more precise measurements of subtype fractions. Methods. We carefully selected our SN sample at a distance of less than 40 Mpc mainly from wide-field surveys conducted over the years from 2016 to 2023. Results. The sample contains a total of 211 SNe, including 109 SNe II, 69 SNe Ia, and 33 SNe Ibc. With the aid of sufficient spectra, we obtained relatively accurate subtype classifications for all SNe in this sample. After corrections for the Malmquist bias, this volumelimited sample yielded fractions of SNe Ia, SNe Ibc, and SNe II of $30.4^{+3.7}_{-11.5}\%$, $16.3^{+3.7}_{-7.4}\%$, and $53.3^{+9.5}_{-18.7}\%$, respectively. In the SN Ia sample, the fraction of the 91T-like subtype becomes relatively low (~5.4%), while that of the 02cx-like subtype shows a moderate increase (~6.8%). In the SN Ibc sample, we find significant fractions of broadlined SNe Ic (~18.0%) and SNe Ibn (~8.8%). The fraction of the 87A-like subtype was determined to be ~2.3%, indicating rare explosions from blue supergiant stars. We find that SNe Ia show a double peak number distribution in S0- and Sc-type host galaxies, which may serve as straightforward evidence for the presence of “prompt” and “delayed” progenitor components that give rise to SN Ia explosions. Several subtypes of SNe such as 02cx-like SNe Ia, broadlined SNe Ic, and SNe IIn (and perhaps SNe Ibn) are found to occur preferentially in less massive spiral galaxies (i.e., with stellar mass <0.5×1010 Mʘ), thus favoring their associations with young stellar progenitors. Moreover, the 02cx-like subtype shows a trend of exploding in the outer skirt of their hosts, which is suggestive of metal-poor progenitors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".