Muztage-Ata 1.93 m协同望远镜测量引力透镜Ia型超新星时间延迟的预测

IF 7.5 1区 物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY
Guanhua Rui, Wenwen Zheng, Zizhao He, Yiping Shu, Bin Hu, Xinzhong Er, Guoliang Li
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引用次数: 0

摘要

强透镜时间延迟测量是一种很有希望解决哈勃张力的方法,与宇宙微波背景分析和局部距离阶梯相比,它提供了一种完全独立的方法。作为哈勃张力的第三方检查,这种方法提供了一个独特的视角。强透镜类星体(glQSO)系统在解决这个问题上显示出了巨大的潜力,达到了令人印象深刻的2%的精度水平。然而,由于glqso的一些内在限制,将精度提高到1%或低于1%是具有挑战性的。幸运的是,强透镜超新星(glSNe)提供了一个更强大的解决方案,这要归功于它们特有的光曲线、显著的亮度变化和其他优势。Muztagh-Ata 1.93米协同望远镜(MOST)是监测强透镜时间延迟的特殊仪器。在本研究中,我们模拟了中国巡天望远镜(CSST)预计首次发现的glSNe - Ia系统的后续多波段光曲线监测。仿真采用W7模型,综合了强透镜效应和微透镜效应。结果表明,在每个历元300 s × 9的曝光下,MOST每年可观测到2个四像系统和14个双像系统。对于最亮的glsnia图像,MOST可以达到约50的信噪比(SNR),而即使是最暗的图像也保持至少7的信噪比。我们使用标准的snia光曲线模板进行拟合,测量了时间延迟。对于二维节拍,MOST实现的相对时延误差小于4.9%,偏差通常保持在0.57%以下。最后,我们进行了哈勃参数估计。对于典型的glne Ia系统,H0误差约为±1.8 km−1 Mpc−1。该结果与LSST的结果相当,甚至更好。这项研究突出了MOST显著提高时间延迟测量精度的能力,为解决哈勃张力提供了一条有希望的途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forecast of gravitationally lensed Type Ia supernovae time delay measurement by Muztage-Ata 1.93 m Synergy Telescope

Strong lensing time delay measurement is a promising method to address the Hubble tension, offering a completely independent approach compared with both the cosmic microwave background analysis and the local distance ladder. As a third-party examination of the Hubble tension, this method provides a unique perspective. Strongly lensed quasar (glQSO) systems have demonstrated significant potential in tackling this issue, achieving an impressive 2% accuracy level. However, advancing to 1% or sub-percent accuracy is challenging due to several intrinsic limitations of glQSOs. Fortunately, strongly lensed supernovae (glSNe) offer a more robust solution, thanks to their characteristic light curve, significant brightness variations, and additional advantages. The Muztagh-Ata 1.93 m Synergy Telescope (MOST) is an exceptional instrument for monitoring strong lensing time delays. In this study, we simulate the follow-up multi-band light curve monitoring for glSNe Ia systems, which are expected to be firstly discovered by the Chinese Survey Space Telescope (CSST). The simulation employs the W7 model, integrating strong lensing and microlensing effects. Our results show that with 300 s × 9 exposures in each epoch, 2 quadruple-image systems and 14 double-image systems are observable by MOST each year. MOST can achieve a signal-to-noise ratio (SNR) of approximately 50 for the brightest images of glSNe Ia, while even the faintest images maintain an SNR of at least 7. Using a standard SNe Ia light curve template for fitting, we measured the time delays. With a 2-d cadence, MOST achieves a relative time delay error of less than 4.9%, with the bias typically remaining below 0.57%. Finally, we performed a Hubble parameter estimation. For a typical glSNe Ia system, the H0 error is about ±1.8 kms−1 Mpc−1. This result is comparable or even better than those from LSST. This study highlights the capability of MOST to significantly advance the precision of time delay measurements, offering a promising path toward resolving the Hubble tension.

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来源期刊
Science China Physics, Mechanics & Astronomy
Science China Physics, Mechanics & Astronomy PHYSICS, MULTIDISCIPLINARY-
CiteScore
10.30
自引率
6.20%
发文量
4047
审稿时长
3 months
期刊介绍: Science China Physics, Mechanics & Astronomy, an academic journal cosponsored by the Chinese Academy of Sciences and the National Natural Science Foundation of China, and published by Science China Press, is committed to publishing high-quality, original results in both basic and applied research. Science China Physics, Mechanics & Astronomy, is published in both print and electronic forms. It is indexed by Science Citation Index. Categories of articles: Reviews summarize representative results and achievements in a particular topic or an area, comment on the current state of research, and advise on the research directions. The author’s own opinion and related discussion is requested. Research papers report on important original results in all areas of physics, mechanics and astronomy. Brief reports present short reports in a timely manner of the latest important results.
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