THE<i>HUBBLE SPACE TELESCOPE</i>CLUSTER SUPERNOVA SURVEY. II. THE TYPE Ia SUPERNOVA RATE IN HIGH-REDSHIFT GALAXY CLUSTERS
Bibliographic record
Abstract
We report a measurement of the Type Ia supernova (SN Ia) rate in galaxy clusters at 0.9 < z < 1.46 from the Hubble Space Telescope Cluster Supernova Survey. This is the first cluster SN Ia rate measurement with detected z > 0.9 SNe. Finding 8 ± 1 cluster SNe Ia, we determine an SN Ia rate of 0.50 +0.23 −0.19 (stat) +0.10 −0.09 (sys) h 2 70 SNuB (SNuB ≡ 10 −12 SNe L −1 ☉, B yr −1 ). In units of stellar mass, this translates to 0.36 +0.16 −0.13 (stat) +0.07 −0.06 (sys) h 2 70 SNuM (SNuM ≡ 10 −12 SNe M −1 ☉ yr −1 ). This represents a factor of ≈5 ± 2 increase over measurements of the cluster rate at z < 0.2. We parameterize the late-time SN Ia delay time distribution (DTD) with a power law: Ψ( t )∝ t s . Under the approximation of a single-burst cluster formation redshift of z f = 3, our rate measurement in combination with lower-redshift cluster SN Ia rates constrains s = −1.41 +0.47 −0.40 , consistent with measurements of the DTD in the field. This measurement is generally consistent with expectations for the "double degenerate" scenario and inconsistent with some models for the "single degenerate" scenario predicting a steeper DTD at large delay times. We check for environmental dependence and the influence of younger stellar populations by calculating the rate specifically in cluster red-sequence galaxies and in morphologically early-type galaxies, finding results similar to the full cluster rate. Finally, the upper limit of one hostless cluster SN Ia detected in the survey implies that the fraction of stars in the intra-cluster medium is less than 0.47 (95% confidence), consistent with measurements at lower redshifts.
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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 | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 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 | 0.003 |
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".