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arXiv 2409.08367astro-ph.CO
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运动学透镜推断 II:利用 $\mathcal{O}$(1) 个星系进行星系团透镜研究

Kinematic Lensing Inference II: Cluster Lensing with $\mathcal{O}$(1) Galaxies

Pranjal R. S., Eric Huff, Elisabeth Krause, Tim Eifler, Spencer Everett, Yu-Hsiu Huang, Jiachuan Xu

AI总结:

本文首次将运动学透镜方法用于Abell 2261星系团,通过联合成像与光谱正向建模分析3个背景盘星系,获得与传统弱透镜一致的剪切估计,且精度较形状噪声提升约十倍。

摘要:

我们展示了利用“运动学透镜”(Kinematic Lensing,KL)首次探测到星系团透镜信号的结果。KL是一种新型弱透镜方法,结合了测光、光谱学和Tully-Fisher关系,能够利用单个源星系进行剪切测量。这是由两篇论文组成、旨在从数据中测量KL信号的系列论文中的第二篇。第一篇论文(arXiv:2209.11811)描述了该推断流程,它对星系成像和光谱进行联合正向建模,并在模拟数据上证明了无偏剪切推断。本文给出了来自星系团Abell 2261的透镜信号测量。我们获得了星系团天区中背景盘星系的光谱观测,这些星系选自CLASH Subaru星表。最终样本包含3个源星系,其余星系因信噪比不足、光谱观测失败以及旋转曲线采样不充分而被剔除。我们将KL推断流程应用于这3个源,发现剪切估计与传统弱透镜测量总体一致。我们源的典型剪切测量不确定度为 $σ(g_+)\approx0.026$,相比弱透镜形状噪声约提升了十倍。我们将目标选择和观测策略确定为未来KL项目改进的关键途径。

英文摘要:

We present the first detection of a cluster lensing signal with `Kinematic Lensing' (KL), a novel weak lensing method that combines photometry, spectroscopy, and the Tully-Fisher relation to enable shear measurements with individual source galaxies. This is the second paper in a two-part series aimed at measuring a KL signal from data. The first paper, arXiv:2209.11811, describes the inference pipeline, which jointly forward models galaxy imaging and spectroscopy, and demonstrates unbiased shear inference with simulated data. This paper presents measurements of the lensing signal from the galaxy cluster Abell 2261. We obtain spectroscopic observations of background disk galaxies in the cluster field selected from the CLASH Subaru catalog. The final sample consists of three source galaxies while the remaining are rejected due to insufficient signal-to-noise, spectroscopic failures, and inadequately sampled rotation curves. We apply the KL inference pipeline to the three sources and find the shear estimates to be in broad agreement with traditional weak lensing measurements. The typical shear measurement uncertainty for our sources is $σ(g_+)\approx 0.026$, which represents approximately a ten-fold improvement over the weak lensing shape noise. We identify target selection and observing strategy as the key avenues of improvement for future KL programs.

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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