THE TYPE II SUPERNOVA RATE IN<i>z</i>∼ 0.1 GALAXY CLUSTERS FROM THE MULTI-EPOCH NEARBY CLUSTER SURVEY
Bibliographic record
Abstract
We present seven spectroscopically confirmed Type II cluster supernovae (SNe II) discovered in the Multi-Epoch Nearby Cluster Survey, a supernova survey targeting 57 low-redshift 0.05 < z < 0.15 galaxy clusters with the Canada–France–Hawaii Telescope. We find the rate of Type II supernovae within R 200 of z ∼ 0.1 galaxy clusters to be 0.026 +0.085 − 0.018 (stat) +0.003 − 0.001 (sys) SNuM. Surprisingly, one SN II is in a red-sequence host galaxy that shows no clear evidence of recent star formation (SF). This is unambiguous evidence in support of ongoing, low-level SF in at least some cluster elliptical galaxies, and illustrates that galaxies that appear to be quiescent cannot be assumed to host only Type Ia SNe. Based on this single SN II we make the first measurement of the SN II rate in red-sequence galaxies, and find it to be 0.007 +0.014 − 0.007 (stat) +0.009 − 0.001 (sys) SNuM. We also make the first derivation of cluster specific star formation rates (sSFR) from cluster SN II rates. We find that for all galaxy types the sSFR is 5.1 +15.8 − 3.1 (stat) ± 0.9(sys) M ☉ yr −1 (10 12 M ☉ ) −1 , and for red-sequence galaxies only it is 2.0 +4.2 − 0.9 (stat) ± 0.4(sys) M ☉ yr −1 (10 12 M ☉ ) −1 . These values agree with SFRs measured from infrared and ultraviolet photometry, and Hα emission from optical spectroscopy. Additionally, we use the SFR derived from our SNII rate to show that although a small fraction of cluster Type Ia SNe may originate in the young stellar population and experience a short delay time, these results do not preclude the use of cluster SN Ia rates to derive the late-time delay time distribution for SNe Ia.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".