Identifying Host Galaxies of Extragalactic Radio Emission Structures using Machine Learning
Bibliographic record
Abstract
Abstract This paper presents an automatic multi-band source cross-identification method based on deep learning to identify the hosts of extragalactic radio emission structures. The aim is to satisfy the increased demand for automatic radio source identification and analysis of large-scale survey data from next-generation radio facilities such as the Square Kilometre Array and the Next Generation Very Large Array. We demonstrate a 97% overall accuracy in distinguishing quasi-stellar objects, galaxies and stars using their optical morphologies plus their corresponding mid-infrared information by training and testing a convolutional neural network on Pan-STARRS imaging and WISE photometry. Compared with an expert-evaluated sample, we show that our approach has 95% accuracy at identifying the hosts of extended radio components. We also find that improving radio core localization, for instance by locating its geodesic center, could further increase the accuracy of locating the hosts of systems with a complex radio structure, such as C-shaped radio galaxies. The framework developed in this work can be used for analyzing data from future large-scale radio surveys.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".