Strong gravitational lensing in the era of wide-area sensitive surveys

广域敏感勘测时代的强引力透镜

• 批准号: 2597317
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2597317
• 关键词: Strong; gravitational; lensing; era; wide

The next decade will witness a revolution in the use of strong gravitational lenses as sensitive probes to address many open problems in cosmology and extragalactic astrophysics. This is enabled by the forthcoming era of wide-area sensitive imaging surveys. Such surveys are essential for the discovery of the very rare strong gravitational lenses. Despite being first observationally confirmed in 1970s, we only know of ~1000s of lenses. This rarity has meant we cannot fully exploit their promising uses. This DPhil project is focussed on the discovery of large samples of strong gravitational lenses, of order 100,000s, in surveys carried out by the Vera C. Rubin Observatory, and the Euclid and Nancy A. Roman Space Telescopes. As such, this is a necessary body of work to maximise the discovery of strong gravitational lenses, providing the community with means to discover large samples of high completeness and purity. The outstanding central problem in strong gravitational lens discovery is the high rate of false positives - among these are e.g., chance alignments of background star forming galaxies with foreground massive ellipticals and high redshift spirals mimicking lensed arcs. To date, supervised machine learning algorithms yield samples that are highly impure (by factors of several). As a result, human visual inspection remains the only means to improve the purity, but it is labour intensive. This project includes an innovative use of discovery algorithms coupled to crowd sourced visual inspection by citizen scientists. This capitalises on the highly successful Zooniverse project Space Warps. As co-founder and -PI of Space Warps, we are uniquely placed to lead this work. The student will initially run Space Warps assisted discovery systems on existing wide area surveys as pre-cursors to the forthcoming large area surveys. The student will explore the connections between machines and visual inspection. We will focus on expansion on the training samples used for machine learning networks as this is a current limitation in the performance of such networks, along with the construction of active learning loops between the algorithms and the citizen science platform. In addition, we will explore integration of enhanced information derived from the photometry and imaging of the candidates through modelling to rank of the candidates. Ranking is vital in the regime of limited follow-up time for confirmation and cherry picking of sub-samples for science driven analysis. We will study sub-samples of the sources particularly to e.g., constrain the mass distributions of z~0.5-1.5 galaxies and the properties of high-redshift galaxies at high spatial resolution afforded by strong gravitational lensing.

未来十年将见证使用强引力透镜作为敏感探测器的革命，以解决宇宙学和河外天体物理学中许多悬而未决的问题。这是由于即将到来的广域敏感成像调查时代。这样的观测对于发现非常罕见的强引力透镜是必不可少的。尽管在20世纪70年代首次被观测证实，但我们只知道大约1000个镜头。这种稀有性意味着我们无法充分利用它们有前途的用途。 这个哲学博士项目的重点是在Vera C.鲁宾天文台、欧几里得天文台和南希A.罗马太空望远镜因此，这是最大限度地发现强引力透镜的必要工作，为社区提供了发现高完整性和纯度的大型样本的手段。强引力透镜发现中突出的中心问题是高误报率-其中包括例如，背景星星形成的星系与前景的大质量椭圆星系和模仿透镜弧的高红移螺旋星系的偶然排列。到目前为止，监督机器学习算法产生的样本是高度不纯的（几个因素）。因此，人工目视检查仍然是提高纯度的唯一手段，但这是劳动密集型的。该项目包括发现算法的创新使用，以及公民科学家的众包视觉检查。这充分利用了非常成功的Zooniverse项目Space Warps。作为Space Warps的联合创始人和PI，我们处于领导这项工作的独特地位。学生将首先在现有的广域调查上运行空间扭曲辅助发现系统，作为即将到来的大面积调查的先导。学生将探索机器和视觉检测之间的联系。我们将专注于扩展用于机器学习网络的训练样本，因为这是此类网络性能的当前限制，沿着算法和公民科学平台之间的主动学习循环的构建。此外，我们亦会研究如何透过模拟，把候选人的光度及影像所得的增强资料，综合起来，以评定候选人的排名。在确认后续时间有限以及为科学驱动的分析挑选子样本的情况下，排名至关重要。 我们将研究来源的子样本，特别是例如，在强引力透镜提供的高空间分辨率下，限制了z~0.5-1.5星系的质量分布和高红移星系的性质。