Slow-blue nuclear hypervariables in PanSTARRS-1
Bibliographic record
Abstract
We discuss 76 large amplitude transients (Δm > 1.5) occurring in the nuclei of galaxies, nearly all with no previously known active galactic nucleus (AGN). They have been discovered as part of the Pan-STARRS1 (PS1) 3π survey, by comparison with Sloan Digital Sky Survey (SDSS) photometry a decade earlier, and then monitored with the Liverpool Telescope, and studied spectroscopically with the William Herschel Telescope (WHT). Based on colours, light-curve shape, and spectra, these transients fall into four groups. A few are misclassified stars or objects of unknown type. Some are red/fast transients and are known or likely nuclear supernovae. A few are either radio sources or erratic variables and so likely blazars. However the majority (∼66 per cent) are blue and evolve slowly, on a time-scale of years. Spectroscopy shows them to be AGN at z ∼ 0.3 − 1.4, which must have brightened since the SDSS photometry by around an order of magnitude. It is likely that these objects were in fact AGN a decade ago, but too weak to be recognized by SDSS; they could then be classed as ‘hypervariable’ AGN. By searching the SDSS Stripe 82 quasar database, we find 15 similar objects. We discuss several possible explanations for these slow-blue hypervariables – (i) unusually luminous tidal disruption events; (ii) extinction events; (iii) changes in accretion state; and (iv) large amplitude microlensing by stars in foreground galaxies. A mixture of explanations (iii) and (iv) seems most likely. Both hold promise of considerable new insight into the AGN phenomenon.
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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 | 0.002 | 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) | 0.002 | 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".