Galactic pulsar with proper motions

In particular, in this paper we focus on their dynamical characteristics and show that an artificial neural network is able to estimate with high accuracy the parameters that control the current positions of a mock population of pulsars. For this purpose, we implement a simplified population-synthesis framework (where selection biases are neglected at this stage) and concentrate on the natal kick-velocity distribution and the distribution of birth distances from the Galactic plane.

By varying these and evolving the pulsar trajectories in time, we generate a series of simulations that are used to train and validate a suitably structured convolutional neural network. We demonstrate that our network is able to recover the parameters governing the distribution of kick velocity and Galactic height with a mean relative error of about 10^-2^. We discuss the limitations of our idealized approach and study a toy problem to introduce selection effects in a phenomenological way by incorporating the observed proper motions of 216 isolated pulsars.