Measuring the ejecta velocities of type Ia supernovae from the pan-STARRS1 medium deep survey
Bibliographic record
Abstract
ABSTRACT There is growing evidence that Type Ia supernovae (SNe Ia) may originate from multiple explosion channels. Previous studies have indicated that the ejecta velocity of SNe Ia is one powerful tool to discriminate between different channels. In this work, we study ∼400 confirmed SNe Ia discovered by the Pan-STARRS1 Medium Deep Survey (PS1-MDS), and obtain a sample of ∼50 SNe Ia that have near-peak $\mathrm{Si}\, {\small II}\, \lambda 6355$ velocity ($v_{\mathrm{Si}\, {\small II}}$) measurements. We investigate the relationships between $v_{\mathrm{Si}\, {\small II}}$ and various parameters, including SN light-curve width, colour, host galaxy properties, and redshift. No significant trends are identified between $v_{\mathrm{Si}\, {\small II}}$ and light-curve parameters. Regarding the host-galaxy properties, we see a significant trend that high-velocity (HV) SNe Ia ($v_{\mathrm{Si}\, {\small II}}\gtrsim 12000$ km s$^{-1}$) tend to reside in more massive galaxies compared to normal velocity (NV) SNe Ia ($v_{\mathrm{Si}\, {\small II}}\lt 12000$ km s$^{-1}$) when combining both the PS1-MDS data set and those from previous low-z studies. While we do not see a significant trend between $v_{\mathrm{Si}\, {\small II}}$ and redshift, HV SNe Ia appear to be more prevalent in low-z samples than in high-z samples. We discuss several possibilities that could potentially contribute to this trend. Furthermore, we investigate the potential bias on SN Ia distances and find no significant difference in Hubble residuals between HV and NV subgroups.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".