arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2504.01302astro-ph.CO
收藏

用于宇宙学和星系的GPU加速引力透镜与动力学(GLaD)建模

GPU-Accelerated Gravitational Lensing & Dynamical (GLaD) Modeling for Cosmology and Galaxies

Han Wang, Sherry H. Suyu, Aymeric Galan, Aleksi Halkola, Michele Cappellari, Anowar J. Shajib, Miha Cernetic

AI总结:

针对强引力透镜质量片简并性导致的哈勃常数测量精度受限问题,提出恒星运动学与透镜数据联合建模的GPU加速框架,在模拟中实现约4%的$H_0$精度,并将计算时间缩短一个数量级。

摘要:

来自强引力透镜类星体的时延距离测量提供了一种确定哈勃常数($H_0$)的稳健且独立的方法。该方法交叉验证了晚期宇宙距离阶梯和早期宇宙微波背景得出的$H_0$估计值。然而,透镜模型中的质量片简并性引入了系统不确定性,限制了精度。动力学建模通过独立观测数据约束质量分布,补充了强透镜效应。我们开发了一种联合建模恒星运动学和透镜数据的方法与软件框架。使用透镜类星体RXJ1131$-$1131的模拟数据,我们证明了高质量运动学数据可以在$H_0$上实现约4%的精度。通过广泛的建模,我们检验了透镜星系中超大质量黑洞的存在以及运动学数据中潜在的系统偏差对$H_0$测量的影响。结果表明,对黑洞质量和轨道各向异性施加先验,或排除中心运动学分箱,可减轻$H_0$估计中的偏差。通过在带有系统偏差的模拟运动学数据上进行测试,我们强调了对运动学系统误差进行亚百分比控制的需求,这在当前技术下是可实现的。此外,我们利用GPU并行化加速贝叶斯推断,将此前长达一个月的过程缩短了一个数量级。该流水线为利用大型数据集推进宇宙学和星系演化研究提供了巨大潜力。

英文摘要:

Time-delay distance measurements from strongly lensed quasars provide a robust, independent method for determining the Hubble constant ($H_0$). This approach cross-checks $H_0$ estimates from the distance ladder in the late universe and the cosmic microwave background in the early universe. However, the mass-sheet degeneracy in lensing models introduces systematic uncertainty, limiting precision. Dynamical modeling complements strong lensing by constraining the mass distribution with independent observational data. We develop a methodology and software framework for joint modeling of stellar kinematics and lensing data. Using simulated data for the lensed quasar RXJ1131$-$1131, we demonstrate that high-quality kinematic data can achieve $\sim$4% precision on $H_0$. Through extensive modeling, we examine the impact of the presence of a supermassive black hole in the lens galaxy and potential systematic biases in kinematic data on $H_0$ measurements. Our results show that imposing priors on black hole mass and orbital anisotropy, or excluding central kinematic bins, mitigates biases in $H_0$ estimates. By testing on mock kinematic data with systematic biases, we highlight the need for sub-percent control of kinematic systematics, which is achievable with current technology. Additionally, we leverage GPU parallelization to accelerate Bayesian inference, reducing a previously month-long process by an order of magnitude. This pipeline offers significant potential for advancing cosmological and galaxy evolution studies with large datasets.

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。

↑