The Green Bank North Celestial Cap Pulsar Survey: A Decade of Discovery
Bibliographic record
Abstract
The Green Bank North Celestial Cap (GBNCC) pulsar survey is the mostsuccessful low-frequency pulsar survey ever carried out. Using the Robert C. Byrd Green Bank Telescope (GBT) to cover 85% of the celestial sphere at a center frequency of 350 MHz, the survey is optimized for finding bright, nearby pulsars, particularly millisecond pulsars (MSPs) in short-orbital period binary systems. Data-taking, which began in 2009, is 95% complete, and we expect to finish the survey in 2021. Here, we provide a broad overview and update of the GBNCC survey, with a focus on recent results. To-date, GBNCC has discovered 190 pulsars, of which 33 are MSPs. Ten MSPs have been included in the North American Nanohertz Observatory for Gravitational Waves with the goal of directly detecting low-frequency gravitational waves. Improvements in our single-pulse detection pipeline have also resulted in the discovery of the first fast radio burst in the survey. In partnership with the Canadian HI Intensity Mapping Experiment(CHIME), we are observing select GBNCC pulsars with an increased cadence, which is greatly accelerating our ability to derive timing solutions. The increased cadence has also allowed us to measure three post-Keplerian parameters in a highly relativistic double neutron star system, providing a test of general relativity that will steadily improve in precision with time. We expect that approximately 50 additional long-period pulsars and 3-8 MSPs will be discovered in the remaining survey regions. A full re-processing of the data using improved interference excision and candidate selection is planned,which may result in additional discoveries.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".