BURSTT: Bustling Universe Radio Survey Telescope in Taiwan
Bibliographic record
Abstract
Abstract Fast Radio Bursts (FRBs) are bright millisecond-duration radio transients that appear about 1000 times per day, all-sky, for a fluence threshold 5 Jy ms at 600 MHz. The FRB radio-emission physics and the compact objects involved in these events are subjects of intense and active debate. To better constrain source models, the Bustling Universe Radio Survey Telescope in Taiwan (BURSTT) is optimized to discover and localize a large sample of rare, high-fluence, and nearby FRBs. This population is the most amenable to multi-messenger and multi-wavelength follow-up, which allows a deeper understanding of source mechanisms. BURSTT will provide horizon-to-horizon sky coverage with a half power field-of-view (FoV) of ∼10 4 deg 2 , a 400 MHz effective bandwidth between 300 and 800 MHz, and subarcsecond localization, which is made possible using outrigger stations that are hundreds to thousands of km from the main array. Initially, BURSTT will employ 256 antennas. After tests of various antenna designs and optimizing the system’s performance, we plan to expand to 2048 antennas. We estimate that BURSTT-256 will detect and localize ∼100 bright (≥100 Jy ms) FRBs per year. Another advantage of BURSTT’s large FoV and continuous operation will be its greatly enhanced monitoring of FRBs for repetition. The current lack of sensitive all-sky observations likely means that many repeating FRBs are currently cataloged as single-event 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".