KARMMA: Mass Mapping Worthy of LSST

宇宙目前正在以越来越快的速度膨胀。为了理解暗能量加速膨胀的起源，必须确定利用引力将宇宙保持在一起的物质的数量。天文学界正在着手进行迄今为止最大和最雄心勃勃的天文调查：鲁宾天文台时空遗产调查（LSST）。调查的一个关键目标是构建有史以来最详细的宇宙物质分布图，从而使我们的宇宙膨胀的宇宙学研究。然而，宇宙中80%的物质是暗的（在任何波长下都不可见），因此我们必须依靠间接的探测方法。这些研究人员将利用引力引起的星系图像失真来推断宇宙的物质密度。他们将实施改进，以实现更高分辨率的质量分布图，并将使用这些地图从LSST数据中提取暗物质和宇宙学参数。PI将共同指导图森少数民族参与科学和技术计划倡议（TIMESTEP），以促进亚利桑那大学本科生参与STEM职业。该补助金将开发KARMMA（Kappa重建质量映射算法），这是一种质量映射算法，旨在对均匀和各向同性对数正态先验的基础密度场进行正向建模。这种正演建模方法在两个关键的改进，目前标准的质量映射技术。1.通过对收敛场进行正演模拟，KARMMA避免了由于测量边界和剪切非局部性之间的相互作用而导致的质量重建中的数值偏差。2.正演建模使我们能够同时采样宇宙学参数和质量图参数，有效地恢复完整的信息内容从质量图约束宇宙学的目的，同时通过物理宇宙学先验规则化未解决的尺度上的聚类统计。目前的KARMMA实现仅限于固定的宇宙学。此外，其分辨率由于数值考虑而受到限制。这两个缺陷将通过使用球谐函数重新实现算法并实现宇宙学和质量图参数的慢/快采样来解决。KARMMA的这些改进将在LSST时代定义质量映射技术的新标准，同时使利用可观测剪切场的全部信息的宇宙学分析成为可能。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The Universe is currently expanding at an ever-increasing rate. To understand the origin of the accelerated expansion due to dark energy, the amount of matter that is using gravity to hold the Universe together must be determined. The astronomical community is embarking on the largest and most ambitious astronomical survey to date: the Rubin Observatory Legacy Survey of Space and Time (LSST). One key goal of the survey is to construct the most detailed map of the matter distribution in our Universe ever created, thereby enabling cosmological studies of our Universe's expansion. However, 80% of the matter of the Universe is dark (invisible at any wavelength), so we must rely on indirect methods of detection. These investigators will use the distortion of galaxy images due to gravity to infer the matter density of the Universe. They will implement improvements that will enable higher resolution mass distribution maps, and they will extract dark matter and cosmological parameters from LSST data using these maps. The PI will co-direct the Tucson Initiative for Minority Engagement in Science and TEchnology Program (TIMESTEP) to promote the participation of University of Arizona undergraduate students in STEM careers. This grant will develop the KARMMA (Kappa Reconstruction for Mass Mapping Algorithm), a mass mapping algorithm designed to forward model the underlying density field subject to a homogeneous and isotropic lognormal prior. This forward modeling approach results in two key improvements over currently standard mass mapping techniques. 1. By forward modeling the convergence field, KARMMA avoids numerical biases in the mass reconstruction due to the interaction between survey boundaries and shear non-locality. 2. Forward modeling enables us to simultaneously sample both cosmological parameters and mass-map parameters, efficiently recovering the full information-content from the mass map for the purposes of constraining cosmology, while regularizing the clustering statistics on unresolved scales through physical cosmological priors. The current KARMMA implementation is restricted to fixed cosmologies. Further, its resolution is limited due to numerical considerations. Both of these deficiencies will be addressed by re-implementing the algorithm using spherical harmonics and enabling a slow/fast sampling of cosmological and mass-map parameters. These improvements to KARMMA will define a new standard in mass-mapping techniques in the era of LSST, while enabling cosmological analyses that utilize the full information of the observable shear field.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.