论文标题

利用未解决的镜头类星体:波动曲线的数学基础

Harnessing Unresolved Lensed Quasars: The Mathematical Foundation of the Fluctuation Curve

论文作者

Bag, Satadru, Sohn, Wuhyun, Shafieloo, Arman, Liao, Kai

论文摘要

强力引力镜头类星体(QSO)已成为强大而新颖的宇宙探针,因为它们可以提供关键的宇宙学信息,例如对哈勃常数的测量,与其他探针无关。尽管即将进行的LSST调查预计将发现$ 10^3-10^4 $镜头QSO,但由于看到,很大一部分将无法解决。类星体固有通量的随机性质使识别镜头的透镜并仅使用未解决的光曲线数据来测量时间延迟。在这方面,Bag等人(2022)基于重建图像光曲线中波动的最小化引入了数据驱动的技术。在本文中,我们深入研究了这种方法的数学基础。我们表明,波动曲线中的透镜信号由关节光曲线的衍生物的自动相关函数(ACF)主导。这解释了为什么波动曲线仅使用关节光曲线才能检测到镜头QSO,而无需对QSO通量变异性做出假设,而不需要任何其他信息。我们表明,关节光曲线的衍生物的ACF比关节光本身的ACF更可靠,因为固有的类星体通量可变性显示出长达几百天的显着自动相关性(因为它们遵循红色功率谱)。此外,我们表明,与关节光曲线的ACF相比,当数据具有明显的观察噪声时,波动方法的最小化提供了更好的精度和回忆。

Strong gravitational lensed quasars (QSOs) have emerged as powerful and novel cosmic probes as they can deliver crucial cosmological information, such as a measurement of the Hubble constant, independent of other probes. Although the upcoming LSST survey is expected to discover $10^3-10^4$ lensed QSOs, a large fraction will remain unresolved due to seeing. The stochastic nature of the quasar intrinsic flux makes it challenging to identify lensed ones and measure the time delays using unresolved light curve data only. In this regard, Bag et al (2022) introduced a data-driven technique based on the minimization of the fluctuation in the reconstructed image light curves. In this article, we delve deeper into the mathematical foundation of this approach. We show that the lensing signal in the fluctuation curve is dominated by the auto-correlation function (ACF) of the derivative of the joint light curve. This explains why the fluctuation curve enables the detection of the lensed QSOs only using the joint light curve, without making assumptions about QSO flux variability, nor requiring any additional information. We show that the ACF of the derivative of the joint light curve is more reliable than the ACF of the joint light curve itself because intrinsic quasar flux variability shows significant auto-correlation up to a few hundred days (as they follow a red power spectrum). In addition, we show that the minimization of fluctuation approach provides even better precision and recall as compared to the ACF of the derivative of the joint light curve when the data have significant observational noise.

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