Machine Learning Pipeline for Fast Radio Burst Host Galaxy Identification Using Multi-Survey Data Integration
Bibliographic record
Abstract
Fast Radio Bursts (FRBs) are enigmatic extragalactic phenomena whose host galaxy identification is crucial for understanding their origins and cosmological applications. This paper presents a proof-of-concept machine learning pipeline for automated FRB host galaxy identification using multi-survey data integration from the Canadian Hydrogen Intensity Mapping Experiment (CHIME), Sloan Digital Sky Survey (SDSS), Gaia, and Two Micron All Sky Survey (2MASS) catalogs. Our probabilistic framework combines spatial, photometric, and morphological information with physics-based feature engineering to create a comprehensive automated identification system. We develop and test our methodology on 508 galaxy candidates from 12 carefully selected FRBs, incorporating 198 engineered features optimized for machine learning algorithms. Validation against 4 literature-confirmed FRB-host associations demonstrates perfect recovery with mean probability$0.804 \pm 0.15$, confirming our approach can reliably identify known hosts. Our optimized Ridge regression model achieves exceptional performance with$\mathbf{R}^{2}= 0.970$and cross-validation stability of 0.007, explaining 97% of the variance in host association probabilities compared to traditional proximity-based methods. Applied to the full candidate sample, the pipeline identifies 2 high-confidence host associations$(\mathrm{P}>0.1)$and 4 medium-confidence candidates for targeted follow-up observations. This automated framework addresses the critical need for scalable host identification methods as FRB detection rates increase exponentially, providing a foundation for population-level studies and cosmological applications.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".