Aperture-corrected spectroscopic type Ia supernova host galaxy properties
Bibliographic record
Abstract
We use type Ia supernova (SN Ia) data obtained by the Sloan Digital Sky Survey-II Supernova Survey (SDSS-II SNS) in combination with the publicly available SDSS DR16 fiber spectroscopy of supernova (SN) host galaxies to correlate SN Ia light-curve parameters andHubbleresiduals with several host galaxy properties. Fixed-aperture fiber spectroscopy suffers from aperture effects: the fraction of the galaxy covered by the fiber varies depending on its projected size on the sky, and thus measured properties are not representative of the whole galaxy. The advent of integral field spectroscopy has provided a way to correct the missing light, by studying how these galaxy parameters change with the aperture size. Here we study how the standard SN host galaxy relations change once global host galaxy parameters are corrected for aperture effects. We recover previous trends on SNHubbleresiduals with host galaxy properties, but we find that discarding objects with poor fiber coverage instead of correcting for aperture loss introduces biases into the sample that affect SN host galaxy relations. The net effect of applying the commonly usedg-band fraction criterion is that intrinsically faint SNe Ia in high-mass galaxies are discarded, thus artificially increasing the height of the mass step by 0.02 mag and its significance. Current and next-generation fixed-aperture fiber-spectroscopy surveys, such as OzDES, DESI, or TiDES with 4MOST, that aim to study SN and galaxy correlations must consider, and correct for, these effects.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".