<i>HUBBLE SPACE TELESCOPE</i> STUDIES OF NEARBY TYPE Ia SUPERNOVAE: THE MEAN MAXIMUM LIGHT ULTRAVIOLET SPECTRUM AND ITS DISPERSION
Bibliographic record
Abstract
We present the first results of an ongoing campaign using the STIS spectrograph on board the Hubble Space Telescope ( HST ), whose primary goal is the study of near-ultraviolet (UV) spectra of local Type Ia supernovae (SNe Ia). Using events identified by the Palomar Transient Factory and subsequently verified by ground-based spectroscopy, we demonstrate the ability to locate and classify SNe Ia as early as 16 days prior to maximum light. This enables us to trigger HST in a non-disruptive mode to obtain near UV spectra within a few days of maximum light for comparison with earlier equivalent ground-based spectroscopic campaigns conducted at intermediate-redshifts, . We analyze the spectra of 12 SNe Ia located in the Hubble flow with 0.01 < z < 0.08. Although a fraction of our eventual sample, these data, together with archival data, already provide a substantial advance over that previously available. Restricting samples to those of similar phase and stretch, the mean UV spectrum agrees reasonably closely with that at intermediate redshift, although some differences are found in the metallic absorption features. A larger sample will determine whether these differences reflect possible biases or are a genuine evolutionary effect. Significantly, the wavelength-dependent dispersion, which is larger in the UV, follows similar trends to those observed at intermediate redshift and is driven, in part, by differences in the various metallic features. While the origin of the UV dispersion remains uncertain, our comparison suggests that it may reflect compositional variations among our sample rather than being predominantly an evolutionary effect.
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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 | 0.004 | 0.002 |
| 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".