SN 2023ixf in Messier 101: The Twilight Years of the Progenitor as Seen by Pan-STARRS
Bibliographic record
Abstract
Abstract The nearby type II supernova, SN 2023ixf in M101 exhibits signatures of early time interaction with circumstellar material in the first week postexplosion. This material may be the consequence of prior mass loss suffered by the progenitor, which possibly manifested in the form of a detectable presupernova outburst. We present an analysis of long-baseline preexplosion photometric data in the g, w, r, i, z, and y filters from Pan-STARRS as part of the Young Supernova Experiment, spanning ∼5000 days. We find no significant detections in the Pan-STARRS preexplosion light curves. We train a multilayer perceptron neural network to classify presupernova outbursts. We find no evidence of eruptive presupernova activity to a limiting absolute magnitude of −7 mag. The limiting magnitudes from the full set of gwrizy (average absolute magnitude ≈ −8 mag) data are consistent with previous preexplosion studies. We use deep photometry from the literature to constrain the progenitor of SN 2023ixf, finding that these data are consistent with a dusty red supergiant progenitor with luminosity log L / L ⊙ ≈ 5.12 and temperature ≈ 3950 K, corresponding to a mass of 14–20 M ⊙.
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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 | 0.000 |
| 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) | 0.002 | 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".