论文标题
部分可观测时空混沌系统的无模型预测
Prospects for constraining interacting dark energy cosmology with gravitational-wave bright sirens detected by future SKA-era pulsar timing arrays
论文作者
论文摘要
储层计算是预测湍流的有力工具,其简单的架构具有处理大型系统的计算效率。然而,其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间,然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用,该系统模拟了湍流的若干特征。我们表明,使用径向基函数作为非线性投影器,即使只有部分观测并且不知道控制方程,也能稳健地捕捉复杂的系统非线性。最后,我们表明,当测量稀疏、不完整且带有噪声,甚至控制方程变得不准确时,我们的网络仍然可以产生相当准确的预测,从而为实际湍流系统的无模型预测铺平了道路。
We explore the constraints on cosmological parameters in interacting dark energy (IDE) models described by energy transfer rates $Q = βH ρ_{\rm de}$ and $Q = βH ρ_{\rm c}$, using simulated gravitational-wave (GW) bright siren data from pulsar timing arrays (PTAs) and the Planck 2018 cosmic microwave background (CMB) data. In particular, we simulate a future PTA observation in the FAST/SKA era with 20 millisecond pulsars (MSPs), each having 20\,ns white noise over a 10-year observation span, and demonstrate that this mock dataset significantly improves the constraint precision of key cosmological parameters such as the Hubble constant $H_0$, matter density $Ω_m$, and the coupling parameter $β$. For the IDE model $Q = βH ρ_{\rm de}$, PTA data alone provides tighter constraints on these parameters than the CMB data alone, primarily due to the high sensitivity of GW standard sirens in probing the late universe. Combining PTA and CMB data further enhances the constraints by 43.6\% for $H_0$, 43.2\% for $Ω_m$, and 44.7\% for $β$, relative to using CMB data alone. In contrast, for $Q = βH ρ_{\rm c}$, the CMB data alone constrains $β$ more tightly than the PTA data, due to the stronger impact of this interaction in the early universe. Nevertheless, the PTA+\,CMB combination still yields improvements of 13.3\% for $H_0$, 22.7\% for $Ω_m$, and 18.2\% for $β$. Increasing the number of MSPs in the PTA further tightens all parameter constraints in both IDE models. Our results highlight the great potential of future PTA observations for significantly improving cosmological parameter estimation in IDE models, offering critical insights into the nature of dark energy and its interaction with dark matter.