Classifying FRB spectrograms using nonlinear dimensionality reduction techniques
Bibliographic record
Abstract
ABSTRACT Fast radio bursts (FRBs) are mysterious astronomical phenomena, and it is still uncertain whether they consist of multiple types. In this study, we use two nonlinear dimensionality reduction algorithms – Uniform Manifold Approximation and Projection (UMAP) and t-distributed stochastic neighbour embedding (t-SNE) – to differentiate repeaters from apparently non-repeaters in FRBs. Based on the first Canadian Hydrogen Intensity Mapping Experiment (CHIME) FRB catalogue, these two methods are applied to standardized parameter data and image data from a sample of 594 sub-bursts and 535 FRBs, respectively. Both methods are able to differentiate repeaters from apparently non-repeaters. The UMAP algorithm using image data produces more accurate results and is a more model-independent method. Our result shows that in general repeater clusters tend to be narrowband, which implies a difference in burst morphology between repeaters and apparently non-repeaters. We also compared our UMAP predictions with the CHIME/FRB discovery of six new repeaters, the performance was generally good except for one outlier. Finally, we highlight the need for a larger and more complete sample of FRBs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".