Classifying stars, galaxies, and AGNs in CLAUDS + HSC-SSP using gradient boosted decision trees
Bibliographic record
Abstract
ABSTRACT Classifying catalogue objects as stars, galaxies, or active galactic nuclei (AGNs) is a crucial part of any statistical study of galaxies. We describe our pipeline for binary (star/galaxy) and multiclass (star/galaxy/Type I AGN/Type II AGN) classification developed for the very deep CLAUDS + HSC-SSP u*grizy data set. Our method uses the XGBoost implementation of gradient boosted trees (GBTs) to train ensembles of models that take photometry, colours, maximum surface brightnesses, and effective radii from all available bands as input, and output the probability that an object belongs to each of the classes under consideration. At iAB < 25 our binary star/galaxy model has AUC = 0.9974 and at the threshold that maximizes our sample’s weighted F1 score, selects a sample of galaxies with 99.7 per cent purity and 99.8 per cent completeness. We test the model’s ability to generalize to objects fainter than those seen during training and find that extrapolation of ∼1−2 mag is reasonable for most applications provided that the galaxies in the training sample are representative of the range of redshifts and colours of the galaxies in the target sample. We also perform an exploratory analysis of the method’s ability to identify AGNs using a small X-ray-selected sample and find that it holds promise for classifying Type I AGN, although it performs less well for Type II AGN. Our results demonstrate that GBTs provide a flexible, robust, and efficient method for performing classification of catalogue objects in large astronomical imaging surveys.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".