The First Radio View of a Type Ibn Supernova in SN 2023fyq: Understanding the Mass-loss History in the Last Decade before the Explosion
Bibliographic record
Abstract
Abstract Supernovae that interact with hydrogen-poor, helium-rich circumstellar material (CSM), known as type Ibn supernovae (SNe Ibn), present a unique opportunity to probe mass-loss processes in massive stars. In this work, we report the first radio detection of an SN Ibn, SN 2023fyq, and characterize the mass-loss history of its stellar progenitor using the radio and X-ray observations obtained over 18 months post-explosion. We find that the radio emission from 58 to 185 days is best modeled by synchrotron radiation attenuated by free–free absorption from a CSM of density ∼10 −18 g cm −3 (∼10 6 ρ ISM ) at a radius of 10 16 cm, corresponding to a mass-loss rate of ∼4 × 10 −3 M ⊙ yr −1 (for a CSM velocity of 1700 km s −1 from optical spectroscopy) from 0.7 to 3 yr before the explosion. This timescale is consistent with the time frame over which pre-explosion optical outbursts were observed. However, our late-time observations at 525 days post-explosion yield nondetections, and the 3 σ upper limits (along with an X-ray nondetection) allow us to infer a drop in the progenitor mass-loss rate at 5–10 yr pre-explosion with M ̇ < 2.5 × 10 −3 M ⊙ yr −1 . These results suggest a shell-like CSM from at most 4 × 10 15 to 2 × 10 16 cm (∼10 5 R ⊙ ), with a CSM density that is roughly consistent with predictions from a merger model for this object. Future radio observations of a larger sample of SNe Ibn will provide key details on the extent and density of their helium-rich CSM.
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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 | 0.000 | 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".