Finding and modelling gravitationally lensed quasars

引力透镜类星体是探测宇宙参数的有用工具。然而，目前他们对宇宙学竞争性约束的贡献受到少量统计数据的阻碍。提议的工作将是在暗能量调查（DES）数据和潜在的其他存档数据中发现更多这样的系统。然后可以对这些系统进行建模，并用于确定有趣的宇宙学和星系天文学。大气畸变对地面望远镜拍摄的深空天文图像的影响，使系统分类变得困难。特别是，在确定其中包含的许多天文系统之前，必须对大型数据集进行缩减。在寻找引力透镜类星体时，在一个红色星系透镜周围有多个蓝色类星体点源，很难分割图像的独立部分（在解混过程中）。这意味着必须考虑其他因素，如它们的形态结构和颜色，以识别相关系统。由于确定了大量候选系统，我将考虑图像不变量来帮助对它们进行分类，因为更多有希望的候选系统将在光谱望远镜上进行跟踪。识别这类系统的另一种方法是寻找其内在可变性。这是由于类星体（一种吸积的超大质量黑洞）的可变性质造成的。这可以在更多的观测时间后对DES数据进行处理。单独的类星体图像的光传播时间不同，因此引入了一个可以测量的时间延迟，因为类星体的内在变异性（通过时间和亮度的移动来匹配两条光曲线）。这引入了两个光曲线之间的相关性。探测这些系统的许多深层数据可能是低分辨率的，以至于无法分辨两个（或更多）类星体图像和星系。我将研究这种混合光曲线如何在许多时代可以用来提取相关的时间延迟信息，以及它如何可以用作一个候选的选择标准透镜类星体。当发现透镜类星体时，它们可以与星系和类星体的光轮廓相匹配，从而提供精确的天体测量和光度测量，然后可以用来确定系统的预期时间延迟，或者如果时间延迟已知，则诸如当今哈勃常数或暗能量状态方程等参数可能受到限制。这些想法导致了许多可以开发的有趣的识别技术。此外，可以探测许多数据集，例如即将发布的GAIA数据。

Gravitationally lensed quasars are useful tools to probe cosmological parameters. However currently their contribution to making competitive constraints in cosmology is hindered by small number statistics. The proposed work would be to find many more of these systems in the Dark Energy Survey (DES) data and potentially other archived data. The systems can then be modelled and used to determine interesting cosmological and galactic astronomy. The effects of atmospheric distortion on deep astronomical images from ground-based telescopes, make classification of systems difficult. In particular large datasets must be reduced before identification of the many astronomical systems contained within them. In searching for gravitationally lensed quasars, in which you expect multiple blue quasar point sources around a red galaxy lens, it can be difficult to segment the separate parts of the image (during deblending). This means that other factors, such as their morphological structure and colours, must be considered to identify the relevant systems. Due to the large number of candidate systems identified, I will consider image invariants to help classify them, as more promising candidates are followed-up on a spectroscopic telescope.Another way to identify such systems is to look for an intrinsic variability. This is due to the variable nature of a quasar (an accreting supermassive black hole). This could be done with the DES data after more observing time. The light travel time for separate quasar images differs and hence introduces a time delay which can be measured because of the intrinsic quasar variability (matching two light curves by shifting in time and brightness). This introduces a correlation between the two light curves. Much deep data detecting these systems can be of low resolution such that the two (or more) quasar images and galaxy cannot be resolved. I will investigate how this blended lightcurve over many epochs can be used to extract relevant time delay information and also how it can be used as a candidate selection criterion for a lensed quasar. As lensed quasars are found, they can be fitted with galaxy and quasar light profiles which give accurate astrometry and photometry, which can then either be used to determine an expected time delay for the system, or if the time delay is known, parameters such as the present day Hubble constant or the dark energy equation of state may be constrained.These ideas lead on to many interesting identification techniques that can be developed. Furthermore many datasets can be probed, such as the upcoming GAIA data release.