Rotation and flipping invariant self-organizing maps with astronomical images: A cookbook and application to the VLA Sky Survey QuickLook images
Bibliographic record
Abstract
Modern wide field radio surveys typically detect millions of objects. Manual determination of the morphologies is impractical for such a large number of radio sources. Techniques based on machine learning are proving to be useful for classifying large numbers of objects. The self-organizing map (SOM) is an unsupervised machine learning algorithm that projects a many-dimensional dataset onto a two- or three-dimensional lattice of neurons. This dimensionality reduction allows the user to visualize common features of the data better and develop algorithms for classifying objects that are not otherwise possible with large datasets. To this aim, we use the PINK implementation of a SOM. PINK incorporates rotation and flipping invariance so that the SOM algorithm may be applied to astronomical images. In this cookbook we provide instructions for working with PINK, including preprocessing the input images, training the model, and offering lessons learned through experimentation. The problem of imbalanced classes can be improved by careful selection of the training sample and increasing the number of neurons in the SOM (chosen by the user). Because PINK is not scale-invariant, structure can be smeared in the neurons. This can also be improved by increasing the number of neurons in the SOM. We also introduce pyink, a Python package used to read and write PINK binary files, assist in common preprocessing operations, perform standard analyses, visualize the SOM and preprocessed images, and create image-based annotations using a graphical interface. A tutorial is also provided to guide the user through the entire process. We present an application of PINK to VLA Sky Survey (VLASS) images. We demonstrate that the PINK is generally able to group VLASS sources with similar morphology together. We use the results of PINK to estimate the probability that a given source in the VLASS QuickLook Catalogue is actually due to sidelobe contamination.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.179 | 0.066 |
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".