Assessment of Astronomical Images Using Combined Machine-learning Models
Bibliographic record
Abstract
Abstract We present a two-component machine-learning-based approach for classifying astronomical images by data quality via an examination of sources detected in the images and image pixel values from representative sources within those images. The first component, which uses a clustering algorithm, creates a proper and small fraction of the image pixels to determine the quality of the observation. The representative images (and associated tables) are ∼800 times smaller than the original images, significantly reducing the time required to train our algorithm. The useful information in the images is preserved, permitting them to be classified into different categories, but the required storage is reduced. The second component, which is a deep neural network model, classifies the representative images. Using ground-based telescope imaging data, we demonstrate that the method can be used to separate “usable” images from those that present some problems for scientific projects—such as images that were taken in suboptimal conditions. This method uses two different data sets as input to a deep model and provides better performance than if we only used the images’ pixel information. The method may be used in cases where large and complex data sets should be examined using deep models. Our automated classification approach achieves 97% agreement when compared to classification generated via manual image inspection. We compare our method with traditional results and show that the method improves the results by about 10%, and also presents more comprehensive outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".