Seeing the Outer Edge of the Infant Type Ia Supernova 2024epr in the Optical and Near Infrared
Bibliographic record
Abstract
We present optical-to-near-infrared (NIR) photometry and spectroscopy of the Type Ia supernova (SN~Ia) 2024epr, including NIR spectra observed within two days of first light. The early-time optical spectra show strong, high-velocity Ca and Si features near rarely-observed velocities at $ 0.1 c$, and the NIR spectra show a “knee.’’ Despite early-time, high-velocity features, SN~2024epr evolves into a normal SN~Ia, albeit with stronger peak-light Ca absorption than other SNe~Ia with the same light curve shape. Although we infer a normal decline rate, Δ m 15 ( B ) = 1.09 ± 0.12 ~mag, from the light-curve rise, SN~2024epr is a Branch”cool’’ object and has red early-time colors ( g − r ≈ 0.15 ~mag at − 10 ~days). The high velocities point to a density enhancement in the outer layers of the explosion, predicted by some models, but thick-shell He-detonation models do not match the smoothly rising light curve or apparent lack of He in our early-time NIR spectra. No current models (e.g., delayed detonation or thin He shell double detonation) appear to reproduce all observed properties, particularly the unusual early-time colors. Such constraints are only possible for SN~2024epr from the earliest optical and NIR observations, highlighting their importance for constraining SN~Ia models. Finally, we identify several literature SNe~Ia with intermediate mass elements at $ 30 000 k m s ^{-1}$ within days after the explosion that evolve into otherwise normal SNe~Ia at peak light, suggesting the early-time spectra of SNe~Ia may hide a broad diversity of observational characteristics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.003 | 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".