类型 Ia 超级星体距离测量中无损压缩的宇宙信息

我们对四个类型 Ia 超级星体 (SNe Ia) 数据集（Pantheon、Pantheon+、DES-Dovekie、Union3）进行模型无关的距离测量，并将每个数据集压缩为在十一个红移节点处的 $\log r_p(z)$ 的值，其中 $r_p(z)$ 为重新缩放后的共动距离。这些服从高斯分布的压缩值及其完整协方差矩阵，完全捕获了每个数据集中的距离-红移关系信息。我们通过这些数据执行马尔可夫链蒙特卡洛 (MCMC) 似然分析，以推断平坦 $\Lambda$CDM、平坦 $w_0 w_a$CDM 以及非参数化暗能量密度 $X(z) \equiv \rho_{\rm DE}(z)/\rho_{\rm DE}(0)$ 的宇宙参数。在所有数据集、flux-平均配置以及三种宇宙模型下， resulting parameter contours and figures of merit reproduce the corresponding full distance-modulus analyses using the original SNe Ia data sets within the statistical sampling noise of the chains, demonstrating that the eleven $\log r_p$ data points are an operationally lossless compression of the cosmological information in the dataset. Our SN Ia data compression enables an analytic analysis that completes in $O(10^{-2})$ s per dataset and reduces the downstream cosmological MCMC to the fast evaluation of an $11$-dimensional Gaussian likelihood, with a per-step cost set by the number of knots and independent of the SNe Ia sample size. Our methodology will benefit the data analysis of future surveys from Euclid, Roman, and LSST, which will deliver SNe Ia samples one to three orders of magnitude larger than current ones。