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arXiv · 1311.3427

PsrPopPy: An open-source package for pulsar population simulations

Sam Bates·Duncan Lorimer·Akshaya Rane·Joe Swiggum
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Abstract

We have produced a new software package for the simulation of pulsar populations, \textsc{PsrPopPy}, based on the \textsc{Psrpop} package. The codebase has been re-written in Python (save for some external libraries, which remain in their native Fortran), utilising the object-oriented features of the language, and improving the modularity of the code. Pre-written scripts are provided for running the simulations in `standard' modes of operation, but the code is flexible enough to support the writing of personalised scripts. The modular structure also makes the addition of experimental features (such as new models for period or luminosity distributions) more straightforward than with the previous code. We also discuss potential additions to the modelling capabilities of the software. Finally, we demonstrate some potential applications of the code; first, using results of surveys at different observing frequencies, we find pulsar spectral indices are best fit by a normal distribution with mean $-1.4$ and standard deviation $1.0$. Second, we model pulsar spin evolution to calculate the best-fit for a relationship between a pulsar's luminosity and spin parameters. We used the code to replicate the analysis of Faucher-Giguère & Kaspi, and have subsequently optimized their power-law dependence of radio luminosity, $L$, with period, $P$, and period derivative, $\dot{P}$. We find that the underlying population is best described by $L \propto P^{-1.39 \pm 0.09} \dot{P}^{0.48 \pm 0.04}$ and is very similar to that found for $γ$-ray pulsars by Perera et al. Using this relationship, we generate a model population and examine the age-luminosity relation for the entire pulsar population, which may be measurable after future large-scale surveys with the Square Kilometer Array.

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Sam Bates, Duncan Lorimer, Akshaya Rane, Joe Swiggum. 2014-01-20. PsrPopPy: An open-source package for pulsar population simulations. https://doi.org/10.1093/mnras%2Fstu157

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