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来自轴子暗区的耦合 Quintessence(精质)

Coupled quintessence from an axion dark sector

Rayff de Souza, Edmund J. Copeland, Jailson Alcaniz

arXiv 2608.05032首次发表:更新:

AI 中文总结

该研究在轴子暗区框架下构建耦合精质模型,结合 DESI 数据统计分析发现其对观测数据拟合优于 ΛCDM,且无需精细调节初始条件。

AI 中文摘要

DESI 合作组的最新观测数据暗示可能偏离标准 ΛCDM 宇宙学模型,更倾向于存在动力学暗能量成分。具体而言,该暗能量的状态方程会进入所谓的 phantom(幽灵)区域,这在正则单标量场场景中难以实现。然而,这种行为可通过相互作用暗区有效描述,其中暗能量状态方程保持在 phantom 分界线以上,而暗物质成分偏离标准冷暗物质演化。本研究在轴子暗区框架下探索这一可能性,暗能量与暗物质均由两个相互作用的类轴子场表示。研究表明,若这些场需发挥相应作用的质量层级要求得到满足,其动力学可有效置于耦合 Quintessence 框架中,在流体描述下,它们的运动源于有源连续性方程。在该机制下,我们利用当前数据对该场景进行统计分析,发现亚普朗克尺度的暗能量轴子衰变常数处于观测范围内,无需对相关场的初始条件进行精细调节。我们还将该模型与 ΛCDM 进行对比,发现该模型对数据的拟合效果更好,且从贝叶斯视角来看具有竞争力。

英文摘要

Recent observational data arising from the DESI collaboration has hinted at a possible departure from the standard $Λ$CDM cosmological model, preferring instead the presence of a dynamical dark energy component. Specifically, the associated equation of state of the dark energy features a crossing into the so-called phantom regime, which is challenging to accommodate in canonical single scalar-field scenarios. However, this behavior can be effectively described by an interacting dark sector, where the specific dark energy equation of state remains above the phantom divide whilst the dark matter component deviates from the standard cold dark matter evolution. In this work, we explore this possibility in the context of an axion dark sector, where both the dark energy and dark matter are represented by two interacting axion-like fields. We show that given the required mass hierarchy for these fields to play such roles, their dynamics can be effectively placed in the coupled quintessence framework, where their motion follows from a sourced continuity equation in the fluid description. In this regime, we perform a statistical analysis of this scenario with current data, finding that a sub-Planckian dark energy axion decay constant stays well within the observational bounds without the need to fine-tune the associated field's initial conditions. We also perform a comparison with $Λ$CDM, where we find that the model provides a better fit to the data while staying competitive from a Bayesian perspective.

Comments16 pages, 8 figures, 2 tables

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