Systematics in Weak Lensing and Galaxy Clustering

弱透镜和星系团的系统学

• 批准号: 2888854
• 金额: --
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2888854
• 关键词: Systematics; Weak; Lensing; Galaxy; Clustering

With a new generation of surveys on the horizon (e.g. Euclid, LSST, DESI) we are entering a new era of high-precision cosmology. The increase in precision and the scale of data collections compared to previous surveys gives us a new insight into cosmological information. The large amount of the data makes fast and computationally cheap analysis difficult, but if this can be done very precise and robust constraints can be achieved. Through matter probes such as cosmic shear analysis and galaxy clustering we can trace the matter distribution in our universe and use this information to constrain cosmology. These methods can be combined in multi-probe methods such as thein 3x2-point correlation function analysis, which uses both shear-shear correlation, clustering, and their cross-term, to allow more precise parameter constraints. However, are our statistical uncertainties are reduced, we must look closer at the systematic uncertainties induced due to assumptions, degeneracies, and generalisations in our analysis. It's possible that some systematic biases previously concealed by statistical uncertainty are large enough to severely limit our constraining power. On top of this, interactions between systematic effects within and between probes may cause further limitations, such as that between intrinsic alignments and photometric redshift uncertainty. Therefore, an important step in preparation for these surveys is to understand and mitigate the limitations systematics may impose upon them.My PhD project revolves around optimising parameter constraints from weak lensing and galaxy clustering analysis, with a focus on the systematic effects and combinations of systematics that may affect the constraining power of upcoming surveys. I am pursuing my PhD as a part of the CDT, and a component of this is taking part in modules teaching data science methods. I will use the techniques I learn in these modules to understand problematic systematic effects and improve the errors they induce. I aim to use machine learning and deep learning techniques to remove systematic effects and decouple systematics. More broadly, I hope to apply these methods to the parameter constraints from the 3x2 correlation function with the aim to improve the speed and constraining power.

随着新一代的观测（例如欧几里得、LSST、DESI）的出现，我们正在进入一个高精度宇宙学的新时代。与以前的调查相比，精确度和数据收集规模的增加使我们对宇宙学信息有了新的认识。大量的数据使得快速且计算成本低廉的分析变得困难，但如果能够做到这一点，就可以实现非常精确和强大的约束。通过物质探测，如宇宙剪切分析和星系聚类，我们可以追踪宇宙中的物质分布，并利用这些信息来约束宇宙学。这些方法可以组合在多探针方法中，例如3x 2点相关函数分析，其使用剪切-剪切相关、聚类及其交叉项，以允许更精确的参数约束。然而，我们的统计不确定性减少，我们必须仔细研究系统的不确定性诱导由于假设，退化，和概括在我们的分析。有可能之前被统计不确定性所掩盖的一些系统性偏差大到足以严重限制我们的约束力。除此之外，探测器内部和探测器之间的系统效应之间的相互作用可能会导致进一步的限制，例如内在排列和光度红移不确定性之间的限制。因此，在准备这些调查的一个重要步骤是了解和减轻限制系统学可能会强加给他们。我的博士项目围绕优化参数约束弱透镜和星系聚类分析，重点是系统的影响和组合的系统学，可能会影响即将到来的调查的约束力。作为CDT的一部分，我正在攻读博士学位，其中一个组成部分是参与数据科学方法的教学模块。我将使用我在这些模块中学到的技术来理解有问题的系统效应，并改善它们引起的错误。我的目标是使用机器学习和深度学习技术来消除系统影响并解耦系统。更广泛地说，我希望将这些方法应用于3x 2相关函数的参数约束，以提高速度和约束能力。