Dust in the Host Galaxies of Supernovae
Bibliographic record
Abstract
We present Spitzer MIPS 24 μm observations of 50 supernova host galaxies at 0.1 < z < 1.7 in the Great Observatories Origins Deep Survey (GOODS) fields. We also discuss the detection of SN host galaxies in SCUBA/850 μm observations of GOODS-N and Spitzer Infrared Spectrograph (IRS) 16 μm observations of GOODS-S. About 60% of the host galaxies of both Type Ia and core-collapse supernovae are detected at 24 μm, a detection rate that is a factor of 1.5 higher than the field galaxy population. Among the 24 μm detected hosts, 80% have far-infrared luminosities that are comparable to or greater than the optical luminosity, indicating the presence of substantial amounts of dust in the hosts. The median bolometric luminosity of the Type Ia SN hosts is ~10 10.5 L ☉ , very similar to that of core-collapse SN hosts. Using the high-resolution HST ACS data, we have studied the variation of rest-frame optical/ultraviolet colors within the 24 μm detected galaxies at z < 1 to understand the origin of the dust emission. The 24 μm detected galaxies have average colors that are redder by ~0.1 mag than the 24 μm undetected hosts, while the latter show greater scatter in internal colors. This suggests that a smooth distribution of dust is responsible for the observed mid- and far-infrared emission. Of the supernovae that have been detected in the GOODS fields, 70% are located within the half-light radius of the hosts, where dust obscuration effects are significant. Although the dust emission that we detect cannot be translated into a line-of-sight A V , we suggest that the factor of 2-3 larger scatter in the peak B - V colors that is seen in the high- z Type Ia supernova sample relative to the low- z supernovae might be partially due to the dust that we detect in the hosts.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".