Probing the Diversity of Type Ia Supernova Light Curves in the Open Supernova Catalog
Bibliographic record
Abstract
Abstract The ever-growing sample of observed supernovae (SNe) enhances our capacity for comprehensive SN population studies, providing a richer data set for understanding the diverse characteristics of Type Ia supernovae (SNe Ia) and possibly those of their progenitors. Here, we present a data-driven analysis of observed SN Ia photometric light curves collected in the Open Supernova Catalog. Where available, we add the environmental information from the host galaxy. We focus on identifying subclasses of SNe Ia without imposing the predefined subclasses found in the literature to date. To do so, we employ an implicit rank-minimizing autoencoder neural network for developing low-dimensional data representations, providing a compact representation of the SN light-curve diversity. When we analyze light curves alone, we find that one of our resulting latent variables is strongly correlated with redshift, allowing us to approximately “de-redshift” the other latent variables describing each event. After doing so, we find that three of our latent variables account for ∼95% of the variance in our sample, and provide a natural separation between 91T and 91bg thermonuclear SNe. Of note, the 02cx subclass is not unambiguously delineated from the 91bg sample in our results, nor do either the overluminous 91T or the underluminous 91bg/02cx samples form a clearly distinct population from the broader sample of “other” SN Ia events. We identify the physical characteristics of SN light curves that best distinguish SNe 91T from SNe 91bg and 02cx, and discuss prospects for future refinements and applications to other classes of SNe as well as other transients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".