We have used millisecond pulsars (MSPs) from the southern High Time Resolution Universe (HTRU) intermediate latitude survey area to simulate the distribution and total population of MSPs in the Galaxy. Our model makes use of the scale factor method,
which estimates the ratio of the total number of MSPs in the Galaxy to the known sample. Using our best fit value for the z-height, z=500 pc, we find an underlying population of MSPs of 8.3(pm 4.2)*10^4 sources down to a limiting luminosity of L_min=0.1 mJy kpc^2 and a luminosity distribution with a steep slope of dlog N/dlog L = -1.45(pm 0.14). However, at the low end of the luminosity distribution, the uncertainties introduced by small number statistics are large. By omitting very low luminosity pulsars, we find a Galactic population above L_min=0.2 mJy kpc^2 of only 3.0(pm 0.7)*10^4 MSPs. We have also simulated pulsars with periods shorter than any known MSP, and estimate the maximum number of sub-MSPs in the Galaxy to be 7.8(pm 5.0)*10^4 pulsars at L=0.1 mJy kpc^2. In addition, we estimate that the high and low latitude parts of the southern HTRU survey will detect 68 and 42 MSPs respectively, including 78 new discoveries. Pulsar luminosity, and hence flux density, is an important input parameter in the model. Some of the published flux densities for the pulsars in our sample do not agree with the observed flux densities from our data set, and we have instead calculated average luminosities from archival data from the Parkes Telescope. We found many luminosities to be very different than their catalogue values, leading to very different population estimates. Large variations in flux density highlight the importance of including scintillation effects in MSP population studies.
We present 75 pulsars discovered in the mid-latitude portion of the High Time Resolution Universe survey, 54 of which have full timing solutions. All the pulsars have spin periods greater than 100 ms, and none of those with timing solutions are in bi
naries. Two display particularly interesting behaviour; PSR J1054-5944 is found to be an intermittent pulsar, and PSR J1809-0119 has glitched twice since its discovery. In the second half of the paper we discuss the development and application of an artificial neural network in the data-processing pipeline for the survey. We discuss the tests that were used to generate scores and find that our neural network was able to reject over 99% of the candidates produced in the data processing, and able to blindly detect 85% of pulsars. We suggest that improvements to the accuracy should be possible if further care is taken when training an artificial neural network; for example ensuring that a representative sample of the pulsar population is used during the training process, or the use of different artificial neural networks for the detection of different types of pulsars.
