The Green Bank North Celestial Cap Pulsar Survey. V. Pulsar Census and Survey Sensitivity

TL;DR

This paper presents a comprehensive pulsar census from the GBNCC survey at 350 MHz, analyzing detected pulsars, refining their parameters, and assessing the survey's sensitivity and expected discoveries based on population models.

Contribution

It introduces a re-analysis pipeline for GBNCC data, providing detailed pulsar measurements, nulling behavior, and constraints on pulsar population models.

Findings

Detected 661 pulsars out of 1413 in the survey region.

Estimated the survey's sky-averaged limiting flux density as 0.74mJy.

Predicted 65 pulsar discoveries in the remaining survey pointings.

Abstract

The Green Bank North Celestial Cap (GBNCC) pulsar survey will cover the entire northern sky ($\delta > -40^\circ$) at 350 MHz, and is one of the most uniform and sensitive all-sky pulsar surveys to date. We have created a pipeline to re-analyze GBNCC survey data to take a 350MHz 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 661. For these we present measured signal-to-noise ratios (S/N), flux densities, pulse widths, profiles, and where appropriate, refined dispersion measurements (647 out of 661) and new or improved spectral indices (276 out of 661 total, 15 new, 261 improved). Detection scans for several hundred sources were reanalyzed in order to inspect pulsars' single pulse behavior and 223 were confirmed to exhibit evidence of nulling. With a detailed analysis of measured and expected S/N values and the evolving radio frequency interference environment at 350MHz, we assess the GBNCC survey's sensitivity as a function of spin period, dispersion measure, and sky position. We find the sky-averaged limiting flux density of the survey to be 0.74mJy. Combining this analysis with PsrPopPy pulsar population simulations, we predict 60/5 non-recycled/millisecond pulsar discoveries in the survey's remaining 21,000 pointings, and we begin to place constraints on population model parameters.