我们通过将Pantheon+编译的Ia型超新星（SNIa）光度距离信息与从局部化的快速射电暴（FRBs）重建的角直径距离进行比较，测试了Etherington宇宙距离-双关系（CDDR）。我们的核心方法是使用人工神经网络（ANNs）从FRBs中驱动数据重建：我们推断出一个平滑的类银河系色散度量关系，并利用其红移导数来恢复H(z)和D_A^FRB(z)，而无需假设膨胀历史的参数形式。可能偏离CDDR的偏差通过三个一参数模型参数化，其中η(z)≡D_L/[(1+z)^2D_A]。我们实现了两种互补的似然方法：（i）使用带有完整Pantheon+协方差的单个SNIa的直接方法，（ii）一种机器学习方法，其中我们在FRB红移网格上重建SN哈勃图，并通过蒙特卡洛和bootstrap实现将SN和FRB的不确定性传播到非对角协方差矩阵中。在FRB重建中，我们将平均的银河系色散度量锚定在z=0，这给出了数据驱动的约束，即平均的宿主/近源贡献DM_host=128.8±34.1 pc cm^{-3}（3σ统计置信度）。我们发现两种似然实现给出了一致的后验分布，并且在当前精度下没有显著的偏离CDDR的证据。
英文摘要：
We test the Etherington cosmic distance-duality relation (CDDR), by comparing Type Ia supernova (SNIa) luminosity-distance information from the Pantheon+ compilation with an angular-diameter-distance reconstructed from localized Fast Radio Bursts (FRBs). The core of our methodology is a data-driven reconstruction from FRBs using artificial neural networks (ANNs): we infer a smooth mean extragalactic dispersion-measure relation and use its redshift derivative to recover $H(z)$ and hence $D_\mathrm{A}^{\rm FRB}(z)$ without assuming a parametric form for the expansion history. Possible deviations from CDDR are parameterized through three one-parameter models of $η(z)\equiv D_\mathrm{L}/[(1+z)^2D_\mathrm{A}]$. We implement two complementary likelihoods: (i) a direct approach using individual SNIa with the full Pantheon+ covariance, and (ii) a machine-learning approach in which we reconstruct the SN Hubble diagram on the FRB redshift grid, propagating SN and FRB uncertainties into non-diagonal covariance matrices via Monte Carlo and bootstrap realizations. Within the FRB reconstruction, we anchor the mean extragalactic dispersion measure at $z=0$, which yields a data-driven constraint on the average host/near-source contribution $\mathrm{DM}_{\rm host}=128.8\pm 34.1\,\mathrm{pc\,cm^{-3}}$ ($3σ$ of statistical confidence). We find that both likelihood implementations give consistent posteriors and no statistically significant evidence for departures from CDDR at the current precision.
Joint Constraints on the Hubble Constant and Cosmic Baryon Density from Localized Fast Radio Bursts and DESI DR2 Baryon Acoustic Oscillations
College of Sciences, Northeastern University（东北大学理学院）；MOE Key Laboratory of Data Analytics and Optimization for Smart Industry, Northeastern University（东北大学教育部智能工业数据优化与分析重点实验室）；National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University（东北大学国家工业智能与系统优化前沿科学中心）
Unveiling the distribution and redshift dependence of host galaxy dispersion measures using localized fast radio bursts
Center for Gravitation and Cosmology, College of Physical Science and Technology, Yangzhou University（扬州大学物理科学与技术学院引力与宇宙学中心）；Department of Physics, Chongqing University（重庆大学物理学院）
Constraining the Baryon Content of Cosmic Filaments Using Localized Fast Radio Bursts and DESI Imaging Data
School of Physics and Astronomy, Sun Yat-Sen University（中山大学物理学院）；CSST Science Center for the Guangdong-Hong Kong-Macau Greater Bay Area（粤港澳大湾区宇宙线科学中心）
Constraining the Baryon Fraction in Extragalactic Diffuse Ionized Gas with 124 Localized Fast Radio Bursts
Hunan Normal University（湖南师范大学）；Purple Mountain Observatory, Chinese Academy of Sciences（中国科学院紫金山天文台）；University of Science and Technology of China（中国科学技术大学）
Fast Radio Burst Cosmology: Hubble Tension and Dark Energy
School of Astronomy and Space Science, Nanjing University（南京大学天文与空间科学学院）；Key Laboratory of Modern Astronomy and Astrophysics (Nanjing University), Ministry of Education（南京大学现代天文与天体物理教育部重点实验室）；Department of Astronomy, University of Science and Technology of China（中国科学技术大学天文系）
Testing the Distance Duality Relation with Cosmological Observations at high Redshift using Artificial Neural Network
Guizhou Normal University（贵州师范大学）；Purple Mountain Observatory, Chinese Academy of Sciences（中国科学院紫金山天文台）；Hunan Normal University（湖南师范大学）；Hunan University of Science and Technology（湖南科技大学）
Cross-correlating galaxies and cosmic dispersion measures: Constraints on the gas-to-halo mass relation from 2MASS galaxies and 133 localized fast radio bursts
The Institute of Statistical Mathematics（统计数学研究所）；Faculty of Science and Technology, Hirosaki University（弘前大学理工学部）；Yukawa Institute for Theoretical Physics, Kyoto University（京都大学汤川理论物理研究所）；Center for Frontier Science, Chiba University（千叶大学前沿科学中心）；Department of Physics, Graduate School of Science, Chiba University（千叶大学理学研究科物理学专攻）；Kavli Institute for the Physics and Mathematics of the Universe (WPI), The University of Tokyo Institutes for Advanced Study, The University of Tokyo（东京大学宇宙物质起源研究机构（WPI），东京大学高等研究院）
