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arXiv 2101.06145astro-ph.HEastro-ph.GA
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用机器学习分析银河系脉冲星分布

Analyzing the Galactic pulsar distribution with machine learning

Michele Ronchi, Vanessa Graber, Alberto Garcia-Garcia, Jose A. Pons, Nanda Rea

摘要:

我们探索通过机器学习推断银河系中子星种群属性的可能性。特别是,在本文中,我们专注于他们的动力学特性,并表明,人工神经网络是能够估计高精度的参数,控制当前的位置的一个模拟人口的mockpopulationsofmartars。为了这个目的,我们实现了一个简化的人口合成框架(选择偏差在这个阶段被忽略),并集中在出生踢速度分布和出生距离银河平面的分布。通过改变这些并及时发展脉冲星轨迹,我们生成了一系列用于训练和验证适当结构的卷积神经网络的模拟。我们证明,我们的网络是能够恢复的参数,约$10^{-2}$的平均相对误差的踢速度和银河系的高度分布。我们讨论了我们的理想化的方法的局限性,并研究了一个玩具问题,引入选择效应的现象学的方式,将观察到的自行216孤立的行星。我们的分析强调,将具有精确自行测量的中子星样本增加$\sim$ 10倍,这是平方公里阵列未来的突破之一,我们可能成功地通过机器学习以高精度约束银河系中中子星的出生空间和反冲速度分布。

英文摘要:

We explore the possibility of inferring the properties of the Galactic neutron star population through machine learning. 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 which 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 kick-velocity and Galactic height distribution 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. Our analysis highlights that increasing the sample of pulsars with accurate proper motion measurements by a factor of $\sim$10, one of the future breakthroughs of the Square Kilometer Array, we might succeed in constraining the birth spatial and kick-velocity distribution of the neutron stars in the Milky Way with high precision through machine learning.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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