Pulsar Timing Arrays Require Hierarchical Models

Rutger van Haasteren
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Abstract

Pulsar timing array (PTA) projects have found evidence of a stochastic background of gravitational waves (GWB) using data from an ensemble of pulsars. In the literature, minimal assumptions are made about the signal and noise processes that affect data from these pulsars, such as pulsar spin noise. These assumptions are encoded as uninformative priors in Bayesian searches, though frequentist approaches make similar assumptions. Uninformative priors are not suitable for (noise) properties of pulsars in an ensemble, and they bias estimates of model parameters such as gravitational-wave signal parameters. Both frequentist and Bayesian searches are affected. In this article, more appropriate priors are proposed in the language of hierarchical Bayesian modeling, where the properties of the ensemble of pulsars are jointly described with the properties of the individual components of the ensemble. Results by PTA projects should be reevaluated using hierarchical models.
脉冲星定时阵列需要分层模型
脉冲星定时阵列(PTA)项目利用脉冲星集合的数据发现了引力波(GWB)随机背景的证据。在文献中,对影响这些脉冲星数据的信号和噪声过程(如脉冲星自旋噪声)做了最低限度的假设。这些假设在贝叶斯搜索中被编码为无信息先验,尽管频数法也有类似的假设。非信息先验并不适合脉冲星集合的(噪声)特性,而且它们会对引力波信号参数等模型参数的估计产生偏差。频繁搜索和贝叶斯搜索都会受到影响。本文用分层贝叶斯建模的语言提出了更合适的先验值,在这种建模中,脉冲星集合的特性是与集合中各个组成部分的特性共同描述的。PTA项目的结果应使用分层模型进行重新评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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