Population Synthesis of Normal Radio and Gamma-ray Pulsars Using Markov Chain Monte Carlo Techniques
Peter L. Gonthier, Caleb D. Billman, and Alice K. Harding

TL;DR
This study uses Markov Chain Monte Carlo techniques to simulate the population of normal pulsars in the Galaxy, comparing simulated and observed distributions to understand pulsar characteristics and identify model parameter regions.
Contribution
It introduces a comprehensive MCMC-based population synthesis method for pulsars, exploring a ten-dimensional parameter space and applying clustering to identify significant regions.
Findings
Reasonably reproduces observed radio pulsar distributions
Fails to match the Fermi pulsar $ ext{P} - ext{ extdollar} ext{ extbackslash} ext{ extdollar}$ distribution
Underproduces young, high-energy pulsars in simulations
Abstract
We present preliminary results of a pulsar population synthesis of normal pulsars from the Galactic disk using a Markov Chain Monte Carlo method to better understand the parameter space of the assumed model. We use the Kuiper test, similar to the Kolmogorov-Smirnov test, to compare the cumulative distributions of chosen observables of detected radio pulsars with those simulated for various parameters. Our code simulates pulsars at birth using Monte Carlo techniques and evolves them to the present assuming initial spatial, kick velocity, magnetic field, and period distributions. Pulsars are spun down to the present, given radio and gamma-ray emission characteristics, filtered through ten selected radio surveys, and a {\it Fermi} all-sky threshold map. Each chain begins with a different random seed and searches a ten-dimensional parameter space for regions of high probability for a total…
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Taxonomy
TopicsSuperconducting Materials and Applications · Particle physics theoretical and experimental studies · Particle Detector Development and Performance
