Realistic galaxy images and improved robustness in machine learning tasks from generative modelling
Bibliographic record
Abstract
ABSTRACT We examine the capability of generative models to produce realistic galaxy images. We show that mixing generated data with the original data improves the robustness in downstream machine learning tasks. We focus on three different data sets: analytical Sérsic profiles, real galaxies from the COSMOS survey, and galaxy images produced with the SKIRT code, from the IllustrisTNG simulation. We quantify the performance of each generative model, using the Wasserstein distance between the distributions of morphological properties (e.g. the Gini-coefficient, the asymmetry, and ellipticity), the surface brightness distribution on various scales (as encoded by the power spectrum), the bulge statistic, and the colour for the generated and source data sets. With an average Wasserstein distance (Fréchet Inception Distance) of 7.19 × 10−2 (0.55), 5.98 × 10−2 (1.45), and 5.08 × 10−2 (7.76) for the Sérsic, COSMOS and SKIRT data set, respectively, our best models convincingly reproduce even the most complicated galaxy properties and create images that are visually indistinguishable from the source data. We demonstrate that by supplementing the training data set with generated data, it is possible to significantly improve the robustness against domain-shifts and out-of-distribution data. In particular, we train a convolutional neural network to denoise a data set of mock observations. By mixing generated images into the original training data, we obtain an improvement of 11 and 45 per cent in the model performance regarding domain-shifts in the physical pixel size and background noise level, respectively.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".