THE MULTI-EPOCH NEARBY CLUSTER SURVEY: TYPE Ia SUPERNOVA RATE MEASUREMENT IN<i>z</i>∼ 0.1 CLUSTERS AND THE LATE-TIME DELAY TIME DISTRIBUTION
Bibliographic record
Abstract
We describe the Multi-Epoch Nearby Cluster Survey, designed to measure the cluster Type Ia supernova (SN Ia) rate in a sample of 57 X-ray selected galaxy clusters, with redshifts of 0.05 < z < 0.15. Utilizing our real-time analysis pipeline, we spectroscopically confirmed twenty-three cluster SNe Ia, four of which were intracluster events. Using our deep Canada–France–Hawaii Telescope/MegaCam imaging, we measured total stellar luminosities in each of our galaxy clusters, and we performed detailed supernova (SN) detection efficiency simulations. Bringing these ingredients together, we measure an overall cluster SN Ia rate within R 200 (1 Mpc) of 0.042 +0.012 − 0.010 +0.010 − 0.008 SNuM (0.049 +0.016 − 0.014 +0.005 − 0.004 SNuM) and an SN Ia rate within red-sequence galaxies of 0.041 +0.015 − 0.015 +0.005 − 0.010 SNuM (0.041 +0.019 − 0.015 +0.005 − 0.004 SNuM). The red-sequence SN Ia rate is consistent with published rates in early-type/elliptical galaxies in the "field." Using our red-sequence SN Ia rate, and other cluster SN measurements in early-type galaxies up to z ∼ 1, we derive the late-time (>2 Gyr) delay time distribution (DTD) of SN Ia assuming a cluster early-type galaxy star formation epoch of z f = 3. Assuming a power-law form for the DTD, Ψ( t )∝ t s , we find s = −1.62 ± 0.54. This result is consistent with predictions for the double degenerate SN Ia progenitor scenario ( s ∼ −1) and is also in line with recent calculations for the double detonation explosion mechanism ( s ∼ −2). The most recent calculations of the single degenerate scenario DTD predicts an order-of-magnitude drop-off in SN Ia rate ∼6–7 Gyr after stellar formation, and the observed cluster rates cannot rule this out.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".