A Panchromatic View of Late-time Shock Power in the Type II Supernova 2023ixf
Bibliographic record
Abstract
Abstract We present multiwavelength observations of the Type II supernova (SN II) 2023ixf during its first 2 yr of evolution. We combine ground-based optical/near-infrared spectroscopy with Hubble Space Telescope far- and near-ultraviolet spectroscopy and James Webb Space Telescope near- and mid-infrared photometry and spectroscopy to create spectral energy distributions of SN 2023ixf at +374 and +620 days postexplosion, covering a wavelength range of ∼0.1–30 μ m. The multiband light curve of SN 2023ixf follows a standard radioactive decay decline rate after the plateau until ∼500 days, at which point shock-powered emission from ongoing interaction between the SN ejecta and circumstellar material (CSM) begins to dominate. This evolution is temporally consistent with 0.3–10 keV X-ray detections of SN 2023ixf and broad “boxy” spectral line emission, which we interpret to signal reprocessing of shock luminosity in a cold dense shell located between forward and reverse shocks. Using the expected absorbed radioactive decay power and the detected X-ray luminosity, we quantify the total shock-powered emission at the +374 and +620 day epochs and find that it can be explained by nearly complete thermalization of the reverse shock luminosity as SN 2023ixf interacts with a continuous, “wind-like” CSM with a progenitor mass-loss rate of M ̇ ≈ 1 0 − 4 M ⊙ yr −1 ( v w = 20 ± 5 km s −1 ). Additionally, we construct multiepoch spectral models from the non-LTE radiative transfer code CMFGEN that contain radioactive decay and shock powers as well as dust absorption, scattering, and emission. We find that models with shock powers of L sh = (0.5–1) × 10 40 erg s −1 and ∼(0.5–1) × 10 −3 M ⊙ of silicate dust in the cold dense shell and/or inner SN ejecta can effectively reproduce the global properties of the late-time (>300 days) UV-to-IR spectra of SN 2023ixf.
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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) | 0.001 | 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".