用物理信息神经网络重构广义巴罗全息暗能量
Reconstructing the generalized Barrow holographic dark energy with physics-informed neural networks
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中文总结 AI 辅助
本研究利用Cosmo-PINN物理信息神经网络框架，结合多种观测数据重构巴罗指数Δ的红移演化，发现其呈温和平滑演化，后验均值倾向负Δ，证实宇宙学观测可探测熵定律相关量的函数行为。
中文摘要 AI 辅助
巴罗全息暗能量将宇宙加速与视界熵可能的量子引力形变联系起来，这种形变由巴罗指数Δ编码。然而，如果这类效应具有尺度依赖性，那么Δ在整个宇宙历史中保持恒定就没有根本理由。本研究使用Cosmo-PINN物理信息神经网络框架，不假设任何特定函数形式，直接从观测数据重构Δ(z)。训练过程中纳入了广义巴罗全息演化方程，并用PantheonPlus超新星、DESI DR2重子声学振荡和宇宙计时器对重构进行约束。我们发现Δ(z)呈现温和且平滑的红移演化，后验均值倾向于负Δ，当包含造父变星校准数据时，这种倾向会增强。不过，Δ=0和恒定负值仍与当前不确定性相容。重构的宇宙学给出了可行的晚期演化，其暗能量状态方程参数w_DE接近-1，且符合预期的向加速膨胀的转变。我们的结果表明，宇宙学观测可以直接探测到基础熵定律本身所包含的某个量的函数行为。
英文摘要
Barrow holographic dark energy connects cosmic acceleration with possible quantum-gravitational deformations of horizon entropy, encoded in the Barrow exponent $Δ$. If such effects are scale dependent, however, there is no fundamental reason for $Δ$ to remain constant throughout cosmic history. In this work we reconstruct $Δ(z)$ directly from observations, without assuming any particular functional form, using the Cosmo-PINN physics-informed neural-network framework. The generalized Barrow holographic evolution equation is incorporated into the training, while PantheonPlus supernovae, DESI DR2 baryon acoustic oscillations and cosmic chronometers constrain the reconstruction. We find a mild and smooth redshift evolution, with the posterior mean favoring negative $Δ$ and this tendency becoming stronger when the Cepheid calibration is included. Nevertheless, $Δ=0$ and constant negative values remain compatible with current uncertainties. The reconstructed cosmology yields a viable late-time evolution, with $w_{\rm DE}$ close to $-1$ and the expected transition to accelerated expansion. Our results demonstrate that cosmological observations can directly probe the functional behavior of a quantity entering the underlying entropy law itself.
