发表机构
Institute of Theoretical Astrophysics; Institut für Theoretische Physik, Universität Heidelberg; New York University Abu Dhabi; Center for Astrophysics and Space Science (CASS)（理论天体物理研究所; 海德堡大学理论物理研究所; 阿布扎比纽约大学; 天体物理学与空间科学中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用DESI和Euclid数据及单环星系功率谱，预测耦合暗能量参数约束，发现包含非线性尺度可显著改进耦合约束，并确定最小可区分耦合值，结论为只有时变耦合模型可被晚期数据探测。
AI 中文摘要
我们利用DESI和Euclid数据以及单环星系功率谱，预测了耦合暗能量模型参数的约束。我们研究了该模型在$1\sigma$水平上与零耦合情形的可区分性，并探讨了参数约束如何依赖于基准耦合$\beta_{\rm fid}$、固定势斜率参数$\nu$、最大波数$k_{\max}$以及不同的先验选择。我们发现，包含轻度非线性尺度可将耦合的约束改进约五倍。然后，我们解决了在$1\sigma$水平上能与零区分的最小$\beta$值问题。我们发现，对于我们的参考情形$k_{\rm max}=0.2 h/$Mpc，$\beta=0.15$大致位于零以上$1\sigma$处。在最乐观的情形下，即$k_{\rm max}=0.3 h/$Mpc且包含关于$\Omega_{m0}$的Planck先验时，该值可降至$0.05$。这些值远大于当前对$\beta$的约束，但后者是在假设$\beta$至少从退耦时期至今保持恒定的情况下获得的，而我们仅使用晚期数据。因此我们得出结论，只有允许时变耦合的模型才能被晚期数据集探测到。
英文摘要
We forecast constraints on the parameters of the coupled dark energy model using DESI and Euclid data with the one-loop galaxy power spectrum. We investigate the distinguishability of our model from the zero-coupling scenario at the $1σ$ level and explore how the parameter constraints depend on the fiducial coupling $β_{\rm fid}$, the fixed potential slope parameter $ν$, the maximum wavenumber $k_{\max}$, and different prior choices. We find that the inclusion of mildly non-linear scales improves the constraints on the coupling by roughly a factor of five. Then, we address the question of which is the minimum value of $β$ that can be distinguished from zero at $1σ$. We find that for our reference case $k_{\rm max}=0.2 h/$Mpc, $β=0.15$ lies roughly $1σ$ above zero. In the most optimistic case with $k_{\rm max}=0.3 h/$Mpc and including a Planck prior on $Ω_{m0}$, this value can be reduced to $0.05$. These values are substantially larger than the current constraints on $β$, but the latter have been obtained assuming $β$ to be constant from at least the decoupling epoch to today, while we only employ late-time data. We conclude therefore that only models that allow for time-varying couplings can be detected with late-time datasets.
