Astrometric search for gravitational lenses in the Gaia DR2
Bibliographic record
Abstract
The Gaia GraL (Gaia Gravitational Lenses) group has devoted its efforts to perform for the first time a whole sky census of multiply imaged quasars by analysing the Gaia DR2 (GDR2) catalogue.The first step in this direction was to produce a list of candidates based on the astrometry and photometry of GDR2 via machine learning algorithm (Extremely Randomised Tree).The best candidates were selected for confirmation by spectroscopy in observations conducted at several observatories worldwide in both hemispheres.From these observations we increased significantly the number of gravitational lens systems, mainly for quadruply imaged quasars.So far, we have confirmed 21 of these structures including 9 quadruple images.We present here a review of this search and in particular, the results achieved with recent observations using the Gemini and NTT telescopes from which we have discovered one of the most extended gravitational lens known to date.Resumo.O Grupo Gaia GraL (Gaia Gravitational Lenses) tem dedicado seus esforços para realizar o primeiro censo completo do céu de imagens múltiplas de quasares analisando o catálogo Gaia DR2 (GDR2).O primeiro passo nesta direção foi produzir uma lista de candidatas baseada na astrometria e fotometria do GDR2 por meio de um algoritmo de aprendizagem por máquina (Extremely Randomised Tree).Os melhores candidatos foram selecionados para confirmação espectroscópica por meio de observações conduzidas em diversos observatórios distribuídos por todo o mundo nos dois hemisférios.A partir destas observações nós ampliamos significativamente o número de sistemas de lentes gravitacionais conhecidas, principalmente imagens de quasares quadruplicadas.Nós confirmamos 21 destas estruturas, incluindo 9 imagens quadruplas.Nós apresentamos aqui uma revisão desta busca, em particular apresentando os resultados d e observações recentes utilizando o Gemini e o telescópio NTT, onde descobrimos uma das mais estendidas lentes gravitacionais conhecidas até o momento.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.012 |
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".