TransientVerse: A comprehensive real-time alert and multiwavelength analysis system for transient astronomical events
Bibliographic record
Abstract
Transient astrophysical events, characterized by short timescales and high-energy radiation, are a key focus of modern astronomy. However, current transient alert systems face challenges, including the distribution of alerts across multiple platforms and inconsistencies in formatting, which hinder the efficient coordination of follow-up observations in multiwavelength and multi-messenger astronomy. This paper presents TransientVerse, an innovative platform designed to integrate and disseminate transient event alerts. The platform integrates an automated ingestion pipeline that aggregates alerts from multiple platforms (e.g., Astronomer’s Telegram (ATel), the Canadian Hydrogen Intensity Mapping Experiment (CHIME) Fast Radio Burst (FRB) Virtual Observatory Event (VOEvent), and the General Coordinates Network (GCN)). It uses large language models to extract structured information from unstructured alerts, storing both forms in separate databases to support efficient tracking and analysis. TransientVerse offers retrospective searches, data visualization, literature integration and linking, and interactive tools for efficient event tracking and follow-up. For repeating FRBs, the platform generates visualized sky maps and detection statistics from CHIME/FRB VOEvent messages, enabling time-range filtering, coordinate switching, and source ranking by burst frequency to support follow-up planning. TransientVerse improves the efficiency of real-time transient event acquisition, lowers the technical barriers for coordinated observations, and provides robust support for multiwavelength and multi-messenger time-domain astronomy, thereby facilitating astrophysics research.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".