SN 2024cld: unveiling the complex mass-loss histories of evolved supergiant progenitors to core collapse supernovae
Bibliographic record
Abstract
Pre-explosion mass loss in supernova (SN) progenitors is a crucial unknown factor in stellar evolution, yet has been illuminated recently by the diverse zoo of interacting transients. We present SN2024cld, a transitional core-collapse SN at a distance of 39 Mpc, straddling the boundary between SN II and SN IIn, showing persistent interaction with circumstellar material (CSM) similar to H-rich SN1998S and PTF11iqb. The SN was discovered and classified just 12h post-explosion via the GOTO-FAST high-cadence program. Optical spectroscopy, photometry, and polarimetry over 220d chart the complex, long-lived interaction in this transient. Early evolution is dominated by CSM interaction, showing a 14d rise to a peak absolute magnitude of g=-17.6 mag, with clear flash-ionisation signatures. SN2024cld also shows a slowly-evolving late time light curve powered by CSM interaction, with high-velocity (6000 km/s) shoulders on a strong multi-component H-alpha profile. Dense polarimetric coverage reveals marked evolution in the photospheric geometry -- peaking at p=2% 10 days post-explosion, and rotating approx. 60 deg as the ejecta sweep more distant CSM. We observe a narrow 60 km/s H-alpha P Cygni feature throughout, associated with pre-shock CSM. SN2024cld represents among the best-observed 98S-like SNe to date, revealing a multi-component CSM structure: a dense, inner aspherical envelope, CSM disk/torus, and tenuous, extended wind. We propose this SN arose from an evolved supergiant progenitor experiencing multiple mass loss episodes in its terminal years, with binary interaction plausibly generating the CSM disk. SN2024cld constrains the progenitors and mass-loss paradigms of 98S-like SNe, unveiling the chaotic ends of evolved supergiant stars from afar.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".