Multi-wavelength observations of the luminous fast blue optical transient AT 2023fhn
Bibliographic record
Abstract
Context. Luminous fast blue optical transients (LFBOTs) are a class of extragalactic transients notable for their rapid rise and fade times, blue colour, and accompanying luminous X-ray and radio emission. Only a handful have been studied in detail since the prototypical example AT 2018cow. Their origins are currently unknown, but ongoing observations of previous and new events are placing ever stronger constraints on their progenitors. Aims. We aim to put further constraints on the LFBOT AT 2023fhn, and LFBOTs as a class, using information from the multi-wavelength transient light curve, its host galaxy, and local environment. Methods. Our primary results were obtained by fitting galaxy models to the spectral energy distribution of AT 2023fhn’s host and local environment, and by modelling the radio light curve of AT 2023fhn as due to synchrotron self-absorbed emission from an expanding blast wave in the circumstellar medium. Results. We find that neither the host galaxy nor circumstellar environment of AT 2023fhn are unusual compared with previous LFBOTs, but that AT 2023fhn has a much lower X-ray to ultraviolet luminosity ratio than previous events. Conclusions. We argue that the variety in ultraviolet-optical to X-ray luminosity ratios among LFBOTs is likely due to viewing angle differences, and that the diffuse, yet young local environment of AT 2023fhn – combined with a similar circumstellar medium to previous events – favours a progenitor system containing a massive star with strong winds. Plausible progenitor models in this interpretation therefore include the mergers of black holes and Wolf-Rayet stars or failed supernovae.
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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) | 0.002 | 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".