Deep chemical tagging
Bibliographic record
Abstract
Context . Reconstructing the formation history of the Milky Way is hindered by stellar migration, which erases kinematic birth signatures. In contrast, stellar chemical abundances remain stable and can be used to trace stars back to their birth environments through chemical tagging. Aims . This study aims to improve chemical tagging by developing a method that leverages kinematic and age information to enhance clustering in chemical space, while remaining grounded in chemistry. Methods . We implement a graph attention auto-encoder that encodes stars as nodes with chemical features and connects them via edges based on orbital similarity and age. The network learns an “informed” chemical space that accentuates coherent groupings. Results . Applied to ~47 000 APOGEE thin disk stars, the method identifies 282 stellar groups. Among them, five out of six open clusters are successfully recovered. Other groups align with the known moving groups Arch/Hat, Sirius, Hyades, and Hercules. Conclusions . Our approach enables chemically grounded yet kinematically and age informed chemical tagging. It significantly improves the identification of coherent stellar populations, offering a framework for future large-scale stellar archaeology efforts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".