Gaia transients in galactic nuclei
Bibliographic record
Abstract
The high spatial resolution and precise astrometry and photometry of the Gaia mission should make it particularly apt at discovering and resolving transients occurring in, or near, the centres of galaxies. Indeed, some nuclear transients are reported by the Gaia Science Alerts (GSA) team, but not a single confirmed Tidal Disruption Event has been published. In order to explore the sensitivity of GSA, we performed an independent and systematic search for nuclear transients using Gaia observations. Our transient search is driven from an input galaxy catalogue (derived from the Sloan Digital Sky Survey Release 12). We present a candidate detection metric which is independent from the existing GSA methodology, to see if Gaia Alerts are biased against the discovery of nuclear transients, and in particular which steps may have an impact. Our technique does require significant manual vetting of candidates, making implementation in the GSA system impractical for daily operations, although it could be run weekly, which for month-to-year long transients would make a scientifically valuable addition. Our search yielded ~480 nuclear transients, 5 of which were alerted and published by GSA. The list of (in some cases ongoing) transients includes candidates for events related to enhanced accretion onto a super-massive black hole and TDEs. An implementation of the detection methodology and criteria used in this paper as an extension of GSA could open up the possibility for Gaia to fulfil the role as a main tool to find transient nuclear activity as predicted in the literature.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".