Identifying the SN 2022acko progenitor with <i>JWST</i>
Bibliographic record
Abstract
ABSTRACT We report on analysis using the JWST to identify a candidate progenitor star of the Type II-plateau (II-P) supernova SN 2022acko in the nearby, barred spiral galaxy NGC 1300. To our knowledge, our discovery represents the first time JWST has been used to localize a progenitor system in pre-explosion archival Hubble Space Telescope (HST) images. We astrometrically registered a JWST NIRCam image from 2023 January, in which the SN was serendipitously captured, to pre-SN HST F160W and F814W images from 2017 and 2004, respectively. An object corresponding precisely to the SN position has been isolated with reasonable confidence. That object has a spectral energy distribution (SED) and overall luminosity consistent with a single-star model having an initial mass possibly somewhat less than the canonical 8 M⊙ theoretical threshold for core collapse (although masses as high as 9 M⊙ for the star are also possible); however, the star’s SED and luminosity are inconsistent with that of a super-asymptotic giant branch star that might be a forerunner of an electron-capture SN. The properties of the progenitor alone imply that SN 2022acko is a relatively normal SN II-P, albeit most likely a low-luminosity one. The progenitor candidate should be confirmed with follow-up HST imaging at late times, when the SN has sufficiently faded. This potential use of JWST opens a new era of identifying SN progenitor candidates at high spatial resolution.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".