The Green Bank North Celestial Cap Pulsar Survey. V. Pulsar Census and Survey Sensitivity
Bibliographic record
Abstract
Abstract The Green Bank North Celestial Cap (GBNCC) pulsar survey will cover the entire northern sky (δ > −40°) at 350 MHz, and is one of the most uniform and sensitive all-sky pulsar surveys to date. We have created a pipeline to reanalyze GBNCC survey data to take a 350 MHz census of all pulsars detected by the survey, regardless of their discovery survey. Of the 1413 pulsars in the survey region, we were able to recover 670. For these we present measured signal-to-noise ratios (S/N), flux densities, pulse widths, profiles, and where appropriate, refined measurements of dispersion measures (DMs) (656 out of 670) and new or improved spectral indices (339 out of 670 total, 47 new, 292 improved). We also measure the period-pulse width relation at 350 MHz to scale as . Detection scans for several hundred sources were reanalyzed in order to inspect pulsars’ single pulse behavior and 223 were found to exhibit evidence of nulling. With a detailed analysis of measured and expected S/N values and the evolving radio frequency interference environment at 350 MHz, we assess the GBNCC survey’s sensitivity as a function of spin period, DM, and sky position. We find the sky-averaged limiting flux density of the survey to be 0.74 mJy. Combining this analysis with PsrPopPy pulsar population simulations, we predict 60/5 nonrecycled/MSP discoveries in the survey’s remaining 21,000 pointings, and we begin to place constraints on population model parameters.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".