A Square Kilometre Array Pulsar Census

TL;DR

This paper addresses how to optimize all-sky blind pulsar surveys with the SKA1 by integrating two population-synthesis approaches—snapshot and evolutionary—with detailed SKA1-Mid and SKA1-Low survey parameterizations. The snapshot method calibrates to Parkes surveys, refining the pulsar spectral-index distribution, while the evolutionary approach uses magneto-rotational evolution and Bayesian-like inference to constrain birth rates and luminosity parameters; together they yield complementary yield forecasts for three composite survey options. Key findings indicate that targeting SKA1-Low and leveraging a composite survey (with Mid Band 2 in the plane) can maximize detections, with AA4 offering up to ~20% higher yields. The results guide practical survey planning, highlight sizable systematic uncertainties in population modeling, and stress the importance of early, well-documented surveys and standardized reporting to refine neutron-star science goals related to dense matter, gravity tests, and gravitational-wave astronomy.

Abstract

Most of the pulsar science case with the Square Kilometre Array (SKA) depends on long-term precision pulsar timing of a large number of pulsars, as well as astrometric measurements of these using very long baseline interferometry (VLBI). But before we can time them, or VLBI them, we must first find them. Here, we describe the considerations and strategies one needs to account for when planning an all-sky blind pulsar survey using the SKA. Based on our understanding of the pulsar population, the performance of the now-under-construction SKA elements, and practical constraints such as evading radio frequency interference, we project pulsar survey yields using two complementary methods for a number of illustrative survey designs, combining SKA1-Low and SKA1-Mid Bands 1 and 2 in a variety of ways. A composite survey using both Mid and Low is optimal, with Mid Band 2 focused in the plane. We find that, given its much higher effective area and survey speed, the best strategy is to use SKA1-Low to cover as much sky as possible, ideally also overlapping with the areas covered by Mid. In our most realistic scenario, we find that an all-sky blind survey with Phase 1 of the SKA with the AA* array assembly will detect $\sim10,000$ slow pulsars and $\sim 800$ millisecond pulsars (MSPs) if SKA1-Mid covers the region within $5°$ of the plane, while higher latitudes will be covered with SKA1-Low. The yield with AA4 is $\sim 20\%$ higher. One could increase these numbers by increasing the range covered by SKA1-Mid Bands 1 and 2, at the cost of a considerably longer survey. The pulsar census will enable us to set new constraints on the uncertain physical properties of the entire neutron star population. This will be crucial for addressing major SKA science questions including the dense-matter equation of state, strong-field gravity tests, and gravitational wave astronomy.

Figures (9)

• Figure 1: Shown is the FFT correction factor as a function of duty cycle, as determined by mbs+20; this is the response when $32$ harmonics are summed. The efficiency is relative to perfectly phase-coherent signal-to-noise estimates, which the fast folding algorithm mbs+23 gets closer to.
• Figure 2: The top panel shows the confidence intervals, on a logarithmic colour scale, for the simulated Parkes $70$ cm survey yields. The black curve can be considered as the range of spectral index parameters, where both the $20$ cm and $70$ cm yields match acceptably. The top left region of the parameter space produces too few pulsars, the bottom right produces too many. The middle panel shows the corresponding information for the combination of the $20$ cm and $6.5$ GHz surveys. Here, the bottom left produces too few pulsars, the top right too many. The bottom panel shows the logarithm of the product of the confidence intervals, demonstrating that spectral index values of $\mu=-1.45 \pm 0.05$ and $\sigma=0.15 \pm 0.15$ match the observed detections best.
• Figure 3: Corner plot with the one- and two-dimensional posterior distributions for five magneto-rotational parameters and two parameters related to the pulsars' intrinsic luminosity. We highlight the medians in light blue and show corresponding values and 95% credible intervals above the panels. These results were obtained using the methodology outlined in prgr25 and correspond to a converged run of a sequential simulation-based inference experiment. The key difference is that the above posteriors were obtained with a different spectral index distribution (see text for details).
• Figure 4: Shown are the cumulative number of elements for the SKA1-Mid and SKA1-Low arrays as a function of distance from the array centres; dishes for SKA1-Mid in the top panel and stations for SKA1-Low in the bottom panel. Both figures show the comparison between AA* (purple) and AA4 (green). For SKA1-Mid, the MeerKAT array standalone (blue) is also shown for comparison.
• Figure 5: The $1.4$ GHz survey speed metric as a function of array radius for SKA1-Mid in the AA* and AA4 configurations, MeerKAT and several other radio telescopes for comparison. We use the resulting curves to model the optimal sub-array choice for untargeted pulsar searching for our pulsar yield analysis. For AA4, acceptable survey speed is found in the diameter range $\sim 400\;\mathrm{m}$ to $\sim 1$ km; note that the horizontal axis shows radius. Given the need to discover binary systems, we choose the inner $1$ km for our further analysis.