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arXiv 2511.08030astro-ph.COastro-ph.GA
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CSST强引力透镜准备:从CSST星系尺度强引力透镜预测的宇宙学限制

CSST Strong Lensing Preparation: Cosmological Constraints Forecast from CSST Galaxy-Scale Strong Lensing

Yangtze University(长江大学);National Astronomical Observatories, Chinese Academy of Sciences(中国科学院国家天文台);University of Chinese Academy of Sciences(中国科学院大学);Zhengzhou University(郑州大学);University of Portsmouth(朴茨茅斯大学);Beijing Normal University(北京师范大学);Sun Yat-Sen University(中山大学)

Hengyu Wu, Yun Chen, Tonghua Liu, Xiaoyue Cao, Tian Li, Hui Li, Nan Li, Ran Li, Tengpeng Xu

AI总结:

CSST利用强引力透镜数据预测宇宙学限制,通过模拟样本测试红移和速度分散测量的不确定性,发现样本量增加显著提升精度,尤其在暗能量参数上达到更严格限制。

摘要:

星系的强引力透镜效应是一种研究宇宙学和星系结构的强大工具。中国空间站望远镜(CSST)将通过发现多达约10万个多尺度强透镜,将该领域革命化,这一数量远超当前样本。为了利用这一庞大数据集的统计功效,我们通过引力-动力学质量组合方法预测其宇宙学限制能力。我们创建了一个现实的模拟透镜样本,并测试红移和速度分散测量的不确定性如何影响结果,在理想、乐观和悲观场景下。我们发现,将样本量从100增加到10,000系统显著提高了精度:在ΛCDM模型中,物质密度参数Ωm的不确定性从0.2降至0.01;在wCDM模型中,暗能量方程状态w的不确定性从0.3降至0.04。拥有10,000个透镜时,我们对暗能量的限制比最新DESI BAO测量结果更紧密两倍。我们还比较了两种参数估计技术——MultiNest采样和贝叶斯分层建模(BHM)。虽然两者达到的精度相似,但BHM提供了更稳健的内禀透镜参数估计,而MultiNest大约快两倍。这项工作建立了一个高效且可扩展的框架,用于下一代强透镜观测的宇宙学分析。

英文摘要:

Strong gravitational lensing by galaxies is a powerful tool for studying cosmology and galaxy structure. The China Space Station Telescope (CSST) will revolutionize this field by discovering up to $\sim$100,000 galaxy-scale strong lenses, a huge increase over current samples. To harness the statistical power of this vast dataset, we forecast its cosmological constraining power using the gravitational-dynamical mass combination method. We create a realistic simulated lens sample and test how uncertainties in redshift and velocity dispersion measurements affect results under ideal, optimistic, and pessimistic scenarios. We find that increasing the sample size from 100 to 10,000 systems dramatically improves precision: in the $Λ$CDM model, the uncertainty on the matter density parameter, $Ω_m$, drops from 0.2 to 0.01; in the $w$CDM model, the uncertainty on the dark energy equation of state, $w$, decreases from 0.3 to 0.04. With 10,000 lenses, our constraints on dark energy are twice as tight as those from the latest DESI BAO measurements. We also compare two parameter estimation techniques -- MultiNest sampling and Bayesian Hierarchical Modeling (BHM). While both achieve similar precision, BHM provides more robust estimates of intrinsic lens parameters, whereas MultiNest is about twice as fast. This work establishes an efficient and scalable framework for cosmological analysis with next-generation strong lensing surveys.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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