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.

随着新一代的调查即将到来（例如Euclid，LSST，DESI），我们正在进入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为我们提供了对宇宙学信息的新见解。大量数据使快速和计算的廉价分析变得困难，但是如果可以做到非常精确且稳健的约束。通过诸如宇宙剪切分析和星系聚类之类的物质探针，我们可以追踪宇宙中的物质分布，并使用此信息来限制宇宙学。这些方法可以用多探针方法组合，例如使用剪切剪切相关，聚类及其跨学期来允许更精确的参数约束。但是，我们的统计不确定性是否减少了，我们必须更仔细地研究由于我们的分析中的假设，退化和概括而引起的系统不确定性。以前通过统计不确定性隐藏的某些系统偏见可能足够大，可以严重限制我们的约束能力。最重要的是，探针内部和探针之间的系统效应之间的相互作用可能会导致进一步的局限性，例如固有比对与光度红移不确定性之间的相互作用。因此，为这些调查做准备的重要一步是了解和减轻系统对系统的限制。我的博士项目围绕着优化弱透镜和星系聚类分析的参数约束，重点关注系统效应和系统组合的系统效应，以影响即将到来的Surveys的约束力量。我正在攻读博士学位作为CDT的一部分，其中的一个组成部分正在参与教学数据科学方法的模块。我将使用我在这些模块中学习的技术来理解有问题的系统效应并改善它们引起的错误。我的目的是使用机器学习和深度学习技术来消除系统的效果并切除系统学。更广泛地说，我希望将这些方法应用于3x2相关函数的参数约束，以提高速度和约束功率。