Machine Learning Techniques for Star Cluster Science
Bibliographic record
Abstract
Machine learning (ML) is a valuable tool for a variety of astronomical applications, including exploratory data analysis, pattern identification and classification of large high-dimensional datasets. In this tutorial, we: introduce a few common ML techniques used for data visualization and exploration provide a conceptual understanding and pros/cons of each method discuss hyper-parameter determination for these models using real astronomical datasets Specifically, this Jupyter notebook walks through a case study that associates field stars and binaries with suspected parent clusters (e.g. "chemo-dynamical tagging" of star clusters) using APOGEE DR17 and Gaia DR3. This tutorial includes two parts: Part 1 focusses on dimensionality reduction algorithms (PCA, t-SNE and UMAP), while Part 2 centres around supervised techniques (k-NN and SVM). The goal of this workshop is to provide the binaries and star clusters community with new robust, easy-to-use ML tools to tackle novel challenges and unexplored avenues in their own research (e.g. identifying unique stellar systems, recognizing multiple populations, etc.). If you have any questions about this notebook, please feel free to reach out to Steffani Grondin (steffani.grondin@astro.utoronto.ca) or Joshua Speagle (j.speagle@utoronto.ca).
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
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".