THE<i>HUBBLE SPACE TELESCOPE</i>CLUSTER SUPERNOVA SURVEY. III. CORRELATED PROPERTIES OF TYPE Ia SUPERNOVAE AND THEIR HOSTS AT 0.9 <<i>z</i>< 1.46
Bibliographic record
Abstract
Using the sample of Type Ia supernovae (SNe Ia) discovered by the Hubble Space Telescope (HST) Cluster Supernova Survey and augmented with HST -observed SNe Ia in the Great Observatories Origins Deep Survey (GOODS) fields, we search for correlations between the properties of SNe and their host galaxies at high redshift. We use galaxy color and quantitative morphology to determine the red sequence in 25 clusters and develop a model to distinguish passively evolving early-type galaxies from star-forming galaxies in both clusters and the field. With this approach, we identify 6 SN Ia hosts that are early-type cluster members and 11 SN Ia hosts that are early-type field galaxies. We confirm for the first time at z > 0.9 that SNe Ia hosted by early-type galaxies brighten and fade more quickly than SNe Ia hosted by late-type galaxies. We also show that the two samples of hosts produce SNe Ia with similar color distributions. The relatively simple spectral energy distributions expected for passive galaxies enable us to measure stellar masses of early-type SN hosts. In combination with stellar mass estimates of late-type GOODS SN hosts from Thomson & Chary, we investigate the correlation of host mass with Hubble residual observed at lower redshifts. Although the sample is small and the uncertainties are large, a hint of this relation is found at z > 0.9. By simultaneously fitting the average cluster galaxy formation history and dust content to the red-sequence scatters, we show that the reddening of early-type cluster SN hosts is likely E ( B − V ) ≲ 0.06. The similarity of the field and cluster early-type host samples suggests that field early-type galaxies that lie on the red sequence may also be minimally affected by dust. Hence, the early-type-hosted SNe Ia studied here occupy a more favorable environment to use as well-characterized high-redshift standard candles than other 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 | 0.001 | 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) | 0.002 | 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".