Bibliographic record
Abstract
Comprehending the evolutionary history of the Milky Way can offer great insight into how both our home Galaxy and others form and grow. However, studying the Milky Way from our position embedded within it is difficult, and this challenge is exacerbated by the fact that our observations of our Galaxy are limited to a snapshot of its current behaviour. Understanding how the Milky Way has evolved over its approximately 13 billion year lifetime requires unique ways of leveraging astrophysical measurements to constrain the Galaxy's history. This study of the Milky Way's present-day properties to reveal its prior evolution is broadly known as Galactic archaeology. The stars of the Milky Way are particularly promising targets for Galactic archaeology, as judicious observations can constrain a wide array of stellar properties. However, using these properties to uncover individual stellar histories can be challenging. A star's photometric signature changes as it ages, and its gravitational interactions with other components of the Galaxy modify its kinematics, erasing most of the evidence of its past motion through the Milky Way. Fortunately, while these other properties change, a star's atmosphere carries for its entire life an imprint of the elemental abundances of the gas from which it formed. Grouping stars based on this chemical information is called chemical tagging, and this technique can identify groups of stars born in the same giant molecular cloud ('birth clusters') through their shared chemical signatures. In this thesis work, I describe my work on chemical tagging, culminating in the first fully blind chemical tagging experiment with a physically motivated clustering algorithm. The birth cluster candidates identified through this process offer a unique avenue of study, constraining not just individual stellar ages, but the star formation and chemical enrichment history of the Milky Way. Chemical tagging thus enables the detailed analysis of previously inaccessible parts of the Galaxy's history, and the application of the technique will radically alter our understanding of the evolution of the Milky Way.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".