论文标题

在PTA数据集中解开多个随机重力波背景源

Disentangling Multiple Stochastic Gravitational Wave Background Sources in PTA Datasets

论文作者

Kaiser, Andrew R., Pol, Nihan S., McLaughlin, Maura A., Chen, Siyuan, Hazboun, Jeffrey S., Kelley, Luke Zoltan, Simon, Joseph, Taylor, Stephen R., Vigeland, Sarah J., Witt, Caitlin A.

论文摘要

在Nanograv合作的最新数据集,欧洲PULSAR计时阵列(PTA),Parkes PTA和国际PTA的最新数据集中,有了有力的证据,这对于评估可能有助于几种天文学和宇宙学来源的影响至关重要,这些源可能有助于机构重力波动波浪背景(GWB)。使用与Pol等人相同的数据集创建和注入技术。 (2021),我们通过创建单个和多个GWB源数据集评估了多个GWB的可分离性。我们使用贝叶斯PTA分析技术来搜索这些注射源,以评估多个天体物理和宇宙学背景的恢复和分离性。由于超级质量的黑洞二进制和基本的背景,由于GW能量密度比为$ω_ {\ MATHRM {pgw}}/ω_ {\ Mathrm {\ mathrm {smbHb} = 0.5 $,以外的过程中,bayes的进一步范围是,因此GWB的基本引力比为$ω_ {\ mathrm {pgw}}/ω_ {\ mathrm {\ mathrm {\ mathrm {\ mathrm {\ mathrm {\ mathrm {\ m mathrm {\ mathrm {\ mathrm {\ mathrm {pgw}} _ {在20年的数据时,我们能够使用当前的PTA方法和技术来限制该密度比下较弱的GWB的光谱指数和幅度分别为64%和110%的分数不确定性。使用这些方法和发现,我们概述了一个基本协议,以在将来的PTA数据集中搜索多个背景。

With strong evidence of a common-spectrum stochastic process in the most recent datasets from the NANOGrav Collaboration, the European Pulsar Timing Array (PTA), Parkes PTA, and the International PTA, it is crucial to assess the effects of the several astrophysical and cosmological sources that could contribute to the stochastic gravitational wave background (GWB). Using the same dataset creation and injection techniques as in Pol et al. (2021), we assess the separability of multiple GWBs by creating single and multiple GWB source datasets. We search for these injected sources using Bayesian PTA analysis techniques to assess recovery and separability of multiple astrophysical and cosmological backgrounds. For a GWB due to supermassive black hole binaries and an underlying weaker background due to primordial gravitational waves with a GW energy density ratio of $Ω_{\mathrm{PGW}}/Ω_{\mathrm{SMBHB}} = 0.5$, the Bayes' factor for a second process exceeds unity at 17 years, and increases with additional data. At 20 years of data, we are able to constrain the spectral index and amplitude of the weaker GWB at this density ratio to a fractional uncertainty of 64% and 110%, respectively, using current PTA methods and techniques. Using these methods and findings, we outline a basic protocol to search for multiple backgrounds in future PTA datasets.

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